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Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Background: Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking. Methods: In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque. Results: Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH−) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment. Conclusions: This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures. [ABSTRACT FROM AUTHOR]

Simple Summary: Androgenetic alopecia (AGA) represents the most common form of hair loss experienced by both men and women. Curcuma aeruginosa Roxb., a plant known for its medicinal properties, has shown promise in reversing this hair loss disorder for its hair growth effects and anti-androgenic effects. Despite its promising potential, the mechanism of action by which it acts remains unknown. As such, this study unveiled how this plant works against hair loss by identifying its bioactive compounds, the gene its targets, and the potential mechanism involved in the therapy of AGA using network pharmacology and molecular docking. The findings revealed insights into how C. aeruginosa can potentially prevent AGA, highlighting its potential for developing new, safe therapies for AGA, benefiting those affected by this condition. Androgenetic alopecia (AGA) is the most prevalent hair loss disorder worldwide, driven by excessive sensitivity or response to androgen. Herbal extracts, such as Curcuma aeruginosa Roxb., have shown promise in AGA treatment due to their anti-androgenic activities and hair growth effects. However, the precise mechanism of action remains unclear. Hence, this study aims to elucidate the active compounds, putative targets, and underlying mechanisms of C. aeruginosa for the therapy of AGA using network pharmacology and molecular docking. This study identified 66 bioactive compounds from C. aeruginosa, targeting 59 proteins associated with AGA. Eight hub genes were identified from the protein–protein interaction network, namely, CASP3, AKT1, AR, IL6, PPARG, STAT3, HIF1A, and MAPK3. Topological analysis of components–targets network revealed trans-verbenol, myrtenal, carvone, alpha-atlantone, and isoaromandendrene epoxide as the core components with potential significance in AGA treatment. The molecular docking verified the binding affinity between the hub genes and core compounds. Moreover, the enrichment analyses showed that C. aeruginosa is involved in hormone response and participates in HIF-1 and MAPK pathways to treat AGA. Overall, this study contributes to understanding the potential anti-AGA mechanism of C. aeruginosa by highlighting its multi-component interactions with several targets involved in AGA pathogenesis. [ABSTRACT FROM AUTHOR]

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Self-assembly of chiral particles with an L-shape is explored by Monte-Carlo computer simulations in two spatial dimensions. For sufficiently high packing densities in confinement, a carpet-like texture emerges due to the interlocking of L-shaped particles, resembling a distorted smectic liquid crystalline layer pattern. From the positions of either of the two axes of the particles, two different types of layers can be extracted, which form distinct but complementary entangled networks. These coarse-grained network structures are then analyzed from a topological point of view. We propose a global charge conservation law by using an analogy to uniaxial smectics and show that the individual network topology can be steered by both confinement and particle geometry. Our topological analysis provides a general classification framework for applications to other intertwined dual networks. [ABSTRACT FROM AUTHOR]

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, by using methods that can process information and recognize patterns. For example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, threestage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances. [ABSTRACT FROM AUTHOR]

Despite remarkable advancements in the treatment of multiple myeloma (MM), relapse remains a challenge. However, the mechanisms underlying this disease remain unclear. This study aimed to identify potential biomarkers that could open new avenues for MM treatment. Microarray data and clinical characteristics of patients with MM were obtained from the Gene Expression Omnibus database. Differential expression analysis and protein–protein interaction (PPI) network construction were used to identify hub genes associated with MM. Predictive performance was further assessed using receiver operating characteristic curves and nomogram construction. Functional enrichment analysis was conducted to investigate possible mechanisms. Mendelian randomization (MR) was used to evaluate the causal relationship between the crucial gene and MM risk. Topological analysis of the PPI network revealed five hub genes associated with MM, with myeloperoxidase (MPO) being the key gene owing to its highest degree and area under the curve values. MPO showed significant differences between patients with MM and controls across all datasets. Functional enrichment analysis revealed a strong association between MPO and immune‐related pathways in MM. MR analysis confirmed a causal relationship between MPO and the risk of MM. By integrating microarray analysis and MR, we successfully identified and validated MPO as a promising biomarker for MM that is potentially implicated in MM pathogenesis and progression through immune‐related pathways. [ABSTRACT FROM AUTHOR]

Network topology plays a key role in determining the characteristics and dynamical behaviors of a network. But in practice, network topology is sometimes hidden or uncertain ahead of time because of network complexity. In this paper, a robust-synchronization-based topology observer (STO) is proposed and applied to solve the problem of identifying the topology of complex delayed networks (TICDNs). In comparison to the existing literature, the proposed STO does not require any prior knowledge about the range of topological parameters and does not have strict limits on topology type. Furthermore, the proposed STO is suitable not only for networks with fixed coupling strength, but also for networks with adaptive coupling strength. Finally, a few comparison examples for TICDNs are used to verify the feasibility and efficiency of the proposed STO, and the results show that the proposed STO outperforms the other methods. [ABSTRACT FROM AUTHOR]

Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as traffic management, urban planning, and risk assessment. To capture the intrinsic characteristics of urban flow, recent efforts have utilized spatial and temporal graph neural networks to deal with the complex dependence between the traffic in adjacent areas. However, existing graph neural network based approaches suffer from several critical drawbacks, including improper graph representation of urban traffic data, lack of semantic correlation modeling among graph nodes, and coarse-grained exploitation of external factors. To address these issues, we propose DiffUFP, a novel probabilistic graph-based framework for UFP. DiffUFP consists of two key designs: (1) a semantic region dynamic extraction method that effectively captures the underlying traffic network topology, and (2) a conditional denoising score-based adjacency matrix generator that takes spatial, temporal, and external factors into account when constructing the adjacency matrix rather than simply concatenation in existing studies. Extensive experiments conducted on real-world datasets demonstrate the superiority of DiffUFP over the state-of-the-art UFP models and the effect of the two specific modules. [ABSTRACT FROM AUTHOR]

Transparent thermal heaters based on metallic networks have gained considerable attention in the last few years as a result of their superior response time, low sheet resistance, and low cost of manufacturing. To increase the mechanical stability and reliability of the thermal heater, it is desirable to embed the metallic network in some form of matrix. Embedding the network, however, changes the nature of thermal conduction making both in‐plane and out‐of‐plane thermal conduction important for ensuring reliability and uniform thermal distribution. The performance of embedded thermal heaters is also significantly influenced by the geometry of the metallic network, both in terms of optical transparency and thermal performance. In this paper, a coupled electro‐thermal model and an electromagnetic model are developed to investigate the properties of an embedded metallic mesh in a polymer matrix. Infrared thermal imaging and ultraviolet‐visible spectrophotometry are used to quantify thermal transport and transparency in the system and to verify the performance of finite element models. A systematic study is then performed to assess the role of network topology both on in‐plane and out‐of‐plane thermal distribution and optical performance. According to numerical analysis, a structure‐property relationship is established which can provide desirable network configurations to optimize performance. [ABSTRACT FROM AUTHOR]

Network embedding is a technique used to generate low-dimensional vectors representing each node in a network while maintaining the original topology and properties of the network. This technology enables a wide range of learning tasks, including node classification and link prediction. However, the current landscape of network embedding approaches predominantly revolves around static networks, neglecting the dynamic nature that characterizes real social networks. Dynamics at both the micro- and macrolevels are fundamental drivers of network evolution. Microlevel dynamics provide a detailed account of the network topology formation process, while macrolevel dynamics reveal the evolutionary trends of the network. Despite recent dynamic network embedding efforts, a few approaches accurately capture the evolution patterns of nodes at the microlevel or effectively preserve the crucial dynamics of both layers. Our study introduces a novel method for embedding networks, i.e., bilayer evolutionary pattern-preserving embedding for dynamic networks (Bi-DNE), that preserves the evolutionary patterns at both the micro- and macrolevels. The model utilizes strengthened triadic closure to represent the network structure formation process at the microlevel, while a dynamic equation constrains the network structure to adhere to the densification power-law evolution pattern at the macrolevel. The proposed Bi-DNE model exhibits significant performance improvements across a range of tasks, including link prediction, reconstruction, and temporal link analysis. These improvements are demonstrated through comprehensive experiments carried out on both simulated and real-world dynamic network datasets. The consistently superior results to those of the state-of-the-art methods provide empirical evidence for the effectiveness of Bi-DNE in capturing complex evolutionary patterns and learning high-quality node representations. These findings validate the methodological innovations presented in this work and mark valuable progress in the emerging field of dynamic network representation learning. Further exploration demonstrates that Bi-DNE is sensitive to the analysis task parameters, leading to a more accurate representation of the natural evolution process during dynamic network embedding. [ABSTRACT FROM AUTHOR]

Traditional threshold-based methods in brain functional network analysis have some drawbacks. First, the process of determining thresholds is often based on trial and error, lacking standardization, and exhibiting strong subjectivity. Second, this subjectivity may lead to the loss of emotion-related information, limiting a comprehensive understanding and accurate identification of underlying neural processes. To overcome these problems, the persistent homology (PH) theory based on topological data analysis was introduced in this study, and a PH-based framework for emotion recognition in functional brain networks was proposed. Firstly, the EEG signals were divided into five frequency bands (δ , θ , α , β , and γ ) and segmented into multiple time series using non-overlapping sliding windows. Secondly, considering the coupling relationship between brain regions in different emotional states, the degree of phase synchronization between channels was calculated using the phase-locking value (PLV), and the PLV-based brain functional network was constructed. The PLV brain functional network was then analyzed with PH to extract multiple persistent topological features and combine them into richer feature vectors. These persistent topological features include persistence landscapes, Betti curves, persistent entropy, amplitudes, and non-diagonal points. Finally, these persistent feature vectors are used as inputs to a classifier and used for emotion recognition using machine learning algorithms and majority voting methods. Experimental analyses were conducted on the DEAP dataset to evaluate the proposed model. Further validation of the model was implemented on the DREAMER dataset and SEED dataset. The results show that the model achieves good results in EEG emotion recognition. The average accuracy reached 89.94, 87.61, and 83.24%, respectively. In this study, we extracted potential features of EEG data by applying PH to the exploration of functional brain networks, which avoided the reliance on manually determined thresholds and enabled a more comprehensive and accurate method of emotion recognition. Compared with traditional functional brain network methods, our method retains more original information related to functional brain networks in a stable and threshold-free manner. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of placing a minimal number of controls to achieve controllability for a class of networked control systems that are based on the original Turing reaction-diffusion model, which is governed by a set of ordinary differential equations with interactions defined by a ring graph. Turing model considers two morphogens reacting and diffusing over the spatial domain and has been widely accepted as one of the most fundamental models to explain pattern formation in a developing embryo. It is of great importance to understand the mechanism behind the various reaction kinetics that generate such a wide range of patterns. As a first step towards this goal, in this paper we study controllability of Turing model for the case of cells connected as a square grid in which controls can be applied to the boundary cells. We first investigate the minimal control placement problem for the diffusion only system. The eigenvalues of the diffusion matrix are classified by their geometric multiplicity, and the properties of the corresponding eigenspaces are studied. The symmetric control sets are designed to categorize control candidates by symmetry of the network topology. Then the necessary and sufficient condition is provided for placing the minimal control to guarantee controllability for the diffusion system. Furthermore, we show that the necessary condition can be extended to Turing model by a natural expansion of the symmetric control sets. Under certain circumstances, we prove that it is also sufficient to ensure controllability of Turing model. [ABSTRACT FROM AUTHOR]

We investigated the molecular underpinnings of variation in immune responses to the live attenuated typhoid vaccine (Ty21a) by analyzing the baseline immunological profile. We utilized gene expression datasets obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE100665) before and after immunization. We then employed two distinct computational approaches to identify potential baseline biomarkers associated with responsiveness to the Ty21a vaccine. The first pipeline (knowledge- based) involved the retrieval of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network construction, and topological network analysis of post-immunization datasets before gauging their pre-vaccination expression levels. The second pipeline utilized an unsupervised machine learning algorithm for data-driven feature selection on pre-immunization datasets. Supervised machine-learning classifiers were employed to computationally validate the identified biomarkers. Baseline activation of NKIRAS2 (a negative regulator of NF-kB signalling) and SRC (an adaptor for immune receptor activation) was negatively associated with Ty21a vaccine responsiveness, whereas LOC100134365 exhibited a positive association. The Stochastic Gradient Descent (SGD) algorithm accurately distinguished vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1% accuracy for the three identified genes, respectively. This dual-pronged novel analytical approach provides a comprehensive comparison between knowledge-based and data-driven methods for the prediction of baseline biomarkers associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the needle further towards the need for development of precise enteric vaccines and on the importance of pre-immunization screening. • Baseline immunological profiles are determiner of post-immunization immune responses. • Immunization with Typhoid vaccine (Ty21a) have shown sub-optimal vaccine efficacies; reasons remain elusive. • Over expression NKIRAS2 , inhibitor of NF-kB signalling and SRC linked to poor vaccine outcome. • LOC100134365 positive marker of Ty21a vaccine induced protection; needs further exploration. • Both knowledge-based and data-driven pipeline can reveal novel insights from gene expression datasets. [ABSTRACT FROM AUTHOR]

Neural network architectures have been used to address multiple real-world problems with high success. Their extension to graph-structured data started recently to be explored. Graph-neural network (GNN) achieved state-of-the-art performance in multiple problems. In highly imbalanced application domains, such as network intrusion problems, GNN was used to model the network topology. However, in this scenario, the class imbalance problem still affects the performance. Another graph-based solution, the graph attention network (GAT) has also been applied to multiple predictive tasks. Although being a promising avenue, graph-based solutions are still under-explored in imbalanced scenarios. This paper proposes two novel graph-based algorithms, the E-ResSAGE and E-ResGAT algorithms, which build on top of the established GraphSAGE and GAT algorithms, respectively. The key idea is to integrate residual learning into the GNN leveraging the available graph information. Residual connections are added as a strategy to deal with the high class imbalance, aiming at retaining the original information and improving the minority classes' performance. A case study on intrusion detection is provided. Extensive experiments on four recent intrusion detection datasets show the excellent performance of our proposed approaches, especially when predicting minority classes. We demonstrate that embedding residuals in graph-based algorithms presents a strong advantage when learning under imbalanced domains. [ABSTRACT FROM AUTHOR]

Distributed switched large-scale systems are composed by dynamically coupled subsystems, in which interactions among subsystems vary over time according a switching signal. This paper presents a distributed robust switched model-based predictive control (DSwMPC) to control such systems. The proposed method guarantees stabilising the origin of the whole closed-loop system and ensures the constraints satisfaction in the presence of an unknown switching signal. In the distributed model-based predictive control (DMPC) used in this work, by considering the interactions among subsystems as an additive disturbance, the effect of the switch is reflected on the dynamic equation, local, and consistency constraint sets of the nominal subsystems. To compensate the effect of switching signal which creates a time-varying network topology, a robust tube-based switched model-based predictive control (RSwMPC) with switch–robust control invariant set as the target set robust to unknown mode switching is used as local controller. The scheme performance is assessed using three typical examples. The simulation results show that the input and state constraints are satisfied by the proposed DSwMPC at all times. They also validate that the closed-loop system converges to the origin. Also, a comparison of the DSwMPC with a centralised SwMPC (CSwMPC) and a decentralised SwMPC (DeSwMPC) are performed. [ABSTRACT FROM AUTHOR]

Aims: Subsyndromal depression (SSD) is common in mild cognitive impairment (MCI). However, the neural mechanisms underlying MCI with SSD (MCID) are unclear. The default mode network (DMN) is associated with cognitive processes and depressive symptoms. Therefore, we aimed to explore the topological organization of the DMN in patients with MCID. Methods: Forty‐two MCID patients, 34 MCI patients without SSD (MCIND), and 36 matched healthy controls (HCs) were enrolled. The resting‐state functional connectivity of the DMN of the participants was analyzed using a graph theoretical approach. Correlation analyses of network topological metrics, depressive symptoms, and cognitive function were conducted. Moreover, support vector machine (SVM) models were constructed based on topological metrics to distinguish MCID from MCIND. Finally, we used 10 repeats of 5‐fold cross‐validation for performance verification. Results: We found that the global efficiency and nodal efficiency of the left anterior medial prefrontal cortex (aMPFC) of the MCID group were significantly lower than the MCIND group. Moreover, small‐worldness and global efficiency were negatively correlated with depressive symptoms in MCID, and the nodal efficiency of the left lateral temporal cortex and left aMPFC was positively correlated with cognitive function in MCID. In cross‐validation, the SVM model had an accuracy of 0.83 [95% CI 0.79–0.87], a sensitivity of 0.88 [95% CI 0.86–0.90], a specificity of 0.75 [95% CI 0.72–0.78] and an area under the curve of 0.88 [95% CI 0.85–0.91]. Conclusions: The coexistence of MCI and SSD was associated with the greatest disrupted topological organization of the DMN. The network topological metrics could identify MCID and serve as biomarkers of different clinical phenotypic presentations of MCI. [ABSTRACT FROM AUTHOR]

The search for motifs—recurrent patterns in network topology—has led to the identification of universal classes of complex systems across diverse fields and has served as a quantitative tool to reveal common properties in both evolved and designed networks. In this study, we investigate the presence and significance of network superfamilies—previously identified through the census of triadic motifs—in the largest data set of dynamic, biological network models. We present triad significance profiles of 71 existing biological network models, all experimentally inspired. The generated data are treated in an unbiased manner and consistently clustered into two classes using several unsupervised techniques. The more prevalent class does exhibit a strong correlation with the superfamily of sensory transmission networks, which are characterized by the feedforward loop motif commonly found in signal-processing systems. Surprisingly, the remaining class shows a better correlation with the superfamily of word-adjacency networks. To better understand this, the results are analysed for varying network size thresholds, and their connection to the effect of model building activity is examined. It is highlighted that the more the model focuses on smaller portions of the regulatory network, as a result of the coarse-graining of the boundary dynamics and the peripheral regions of the network, the more its topology starts resembling that of 'sentences' of word-adjacency networks. [ABSTRACT FROM AUTHOR]

During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to survival analysis using topological data analysis (TDA) within Bayesian statistics combined with machine learning algorithms suitable to time-to-event data. The paper brings into the analysis aspects of topological invariance through what is known as persistence homology. TDA demonstrates the existence and statistical significance of a kind of unmeasured heterogeneity originating from the topology of the data as a whole. Combined with machine learning tools persistence homology provides us with new tools to construct a rich set of ways to analyze data and build predictive models that are optimized using inherent topological invariants such as one-dimensional loops as regularization. Specifically, this paper incorporates persistent homology effects in different ways in the analysis of survival data through the technique of functional principal component analysis (FPCA): first, by using topological invariants converted into FPCA factors that shape Bayesian statistical analysis of time-to-event data; second, by using FPCA measures of topological invariants in regularizing the process of optimizing the data and the posterior distributions of the Bayesian estimation; three, by using FPCA factors of measures of topological invariants in machine learning algorithms and deep neural networks suitable for analyzing survival data as a way of going beyond usual parametric and semi-parametric models of survival analysis. The approach is illustrated through a running example of multi-frailty survival analysis of democracies in the period of 1950–2010. [ABSTRACT FROM AUTHOR]

This study focuses on a network topology design problem with minimum cost subject to a reliability constraint (NTD-CR). The computational complexity of NTD-CR increases exponentially with an increase in the number of nodes and edges, necessitating the use of efficient algorithms in large-scale networks. Herein, variable neighborhood descent (VND) and weighting local search (WLS) algorithms, along with their hybrid WLS-VND, are used to solve this problem. The VND enables effective exploration within neighborhoods dictated by the characteristics of the problem, whereas WLS employs a dynamic penalty coefficient and weighting function to efficiently navigate the solution space. Numerical experiments confirmed that WLS-VND produced high-quality solutions with reduced dependence on the initial parameter settings. This result aligns with our objectives of developing high-performing practical algorithms for real-world applications and minimizing the need for laborious parameter tuning. Furthermore, WLS-VND holds promise for designing cost-effective and reliable network infrastructures. [ABSTRACT FROM AUTHOR]

Research background: International capital flows show decreasing trend after each financial crisis as countries become unwilling to invest in risky counterparties. Recent discussions over structural changes within the global financial network suggest that its structure may be still undergoing some changes. Uncovered weaknesses of over-connectedness with unstable unally countries during global financial crisis were further highlighted by recent Covid-19 pandemic. However, previous research suggest that important structural changes happened after global financial crisis. Thus, such situation raises a question whether post global financial crisis multiplex network structure is long-term and is it exhibiting re-globalization pattern as suggested by re-globalization megatrend. Purpose of the article: The main objective of the paper is to assess post global financial crisis multiplex financial network structure longevity and to identify whether it exhibits reglobalization pattern. Methods: Multiplex financial network mapping, network topological analysis and structure longevity methods were employed to evaluate network connectedness, topological structural properties of multiplex financial network and its' structure longevity. 5-layer multiplex financial network was mapped for different types of capital flows for each layer: net direct and portfolio investments in debt, equity and net banking assets for each year in the post global financial crisis period 2009-2020. Each network layer employed bilateral data from up to 234 countries of the world. Next step of research employed a set of network-level measures defining topological features of each network in the period of 2009-2020 in order to capture the trend of structural changes and evidence of re-globalization. The final stage analyzes multiplex financial network structure by layer and country longevity. Findings & value added: Analysis reveals that stock (positions) multiplex financial network aggregate connectedness, i.e., density and value, increased during post-global financial crisis period until Covid-19 pandemic period, thus, suggesting that globalization in terms of stock is continuing. Topological structural properties of multiplex financial network changed as number of strongly and weakly connected countries decreased and more countries became included in network. Longevity analysis of layer and country structure within multiplex financial network reveal that banking and debt investments are increasing since 2018, while equity part is decreasing. Developed countries, i.e., the United Kingdom and Germany, decrease in multiplex financial network structure, while developing, especially from Asia region (i.e., Hong Kong SAR, China and Singapore) increase. Such empirical results support the re-globalization megatrend in terms of investment type and counterparty structure as highlighted by most recent scientific discussion showing that it started early after global financial crisis. Research also uncovers some regional trends of Asia region increasing in investment network structure, while Europe decreasing. As developing countries attract more and more investment and further develop, global cost saving and, thus, returns from global investment into these countries may be decreasing, resulting in lower gain from globalization. Our analysis may facilitate investment strategy decisions by suggesting that even though gains from globalization might be decreasing, instead of localization, countries could follow ally investment strategy to manage the risk of over-dependency from unstable counterparty while preserving globalization wealth. [ABSTRACT FROM AUTHOR]

Control and characterization of networks are paramount steps in the development of many quantum technologies. Even for moderate-sized networks, this amounts to exploring an extremely vast parameter space in search for the couplings defining the network topology. Here, we explore the use of a genetic algorithm to retrieve the topology of a network from the measured probability distribution obtained from the evolution of a continuous-time quantum walk on the network. We show that we can successfully retrieve the topology of different networks with efficiencies above 70% in all the examined scenarios and that the algorithm is capable of efficiently retrieving the required information even in the presence of noise. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) bring both potential and difficulties for emergency applications, including packet loss and changes in network topology. UAVs are also quickly taking up a sizable portion of the airspace, allowing Flying Adhoc NETworks (FANETs) to conduct effective ad hoc missions. Therefore, building routing protocols for FANETs is difficult due to flight restrictions and changing topology. To solve these problems, a bio-inspired route selection technique is proposed for FANET. A combined trustworthy and bioinspired-based transmission strategy is developed as a result of the growing need for dynamic and adaptable communications in FANETs. The fitness theory is used to assess direct trust and evaluate credibility and activity to estimate indirect trust. In particular, assessing UAV behavior is still a crucial problem in this field. It recommends fuzzy logic, one of the most widely utilized techniques for trusted route computing, for this purpose. Fuzzy logic can manage complicated settings by classifying nodes based on various criteria. This method combines geocaching and unicasting, anticipating the location of intermediate UAVs using 3-D estimates. This method guarantees resilience, dependability, and an extended path lifetime, improving FANET performance noticeably. Two primary features of FANETs that shorten the route lifetime must be accommodated in routing. First, the collaborative nature necessitates communication and coordination between the flying nodes, which uses a lot of energy. Second, the flying nodes' highly dynamic mobility pattern in 3D space may cause link disconnection because of their potential dispersion. Using ant colony optimization, it employs trusted leader drone selection within the cluster and safe routing among leaders. a fuzzy-based UAV behavior analytics is presented for trust management in FANETs. Compared to existing protocols, the simulated results demonstrate improvements in delay routing overhead in FANET. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the rendezvous problem of discrete-time multi-agent systems. Each agent is equipped with the same sensing, computing, and motion-control capabilities to achieve rendezvous based on the neighbors' states. First, a convex hull combination algorithm (CHCA) is designed, in which each agent solves a convex problem composed of perceived neighbors in the sensing region and chooses an optimal control strategy to move to the next position with guaranteed connectivity under low-density network topologies. Second, the relative neighborhood graph is incorporated into the CHCA (RNCHCA) as the constraint set to adapt to the high-density network topologies. The convergence and connectivity of the proposed algorithms are proved based on the geometric concept and case analyses. Finally, a large number of simulation results show that under the initially connected topologies with different densities, the RNCHCA can achieve a higher rendezvous speed than that achieved by the traditional circumcenter algorithm, particularly under high-density network topologies. [ABSTRACT FROM AUTHOR]

Accurate line parameters are the basis for the optimal control and safety analysis of distribution networks. The lack of real-time monitoring equipment in grids has meant that data-driven identification methods have become the main tool to estimate line parameters. However, frequent network reconfigurations increase the uncertainty of distribution network topologies, creating challenges in the data-driven identification of line parameters. In this paper, a line parameter identification method compatible with an uncertain topology is proposed, which simplifies the model complexity of the joint identification of topology and line parameters by removing the unconnected branches through noise reduction. In order to improve the solving accuracy and efficiency of the identification model, a two-stage identification method is proposed. First, the initial values of the topology and line parameters are quickly obtained using a linear power flow model. Then, the identification results are modified iteratively based on the classical power flow model to achieve a more accurate estimation of the grid topology and line parameters. Finally, a simulation analysis based on IEEE 33- and 118-bus distribution systems demonstrated that the proposed method can effectively realize the estimation of topology and line parameters, and is robust with regard to both measurement errors and grid structures. [ABSTRACT FROM AUTHOR]

Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. An aviation network is a typical complex network, and accurately identifying the key city nodes in the aviation network can help us better prevent network attacks and control the spread of diseases. In this paper, a method for identifying key nodes in undirected weighted networks, called weighted Laplacian energy centrality, was proposed and applied to an aviation network constructed from real flight data. Based on the analysis of the topological structure of the network, the paper recognized critical cities in this network, then simulation experiments were conducted on key city nodes from the perspectives of network dynamics and robustness. The results indicated that, compared with other methods, weighted Laplacian energy centrality can identify the city nodes with the most spreading influence in the network. From the perspective of network robustness, the identified key nodes also have the characteristics of accurately and quickly destroying network robustness. [ABSTRACT FROM AUTHOR]

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect. [ABSTRACT FROM AUTHOR]

Attributed network embedding aims to extract latent features of complex networks from structural topology and node attributes. Existing embedding models either use two separate learning processes to capture the complementarity of network topology and node attributes, or one process to capture their consistency. However, it is rare to simultaneously consider the complementarity and consistency of the network topology and node attributes in a single model, and network noise is often ignored. To address these issues, an unsupervised adversarial enhanced attributed network embedding model is proposed in this paper, called AEANE. The model firstly designs a novel feature extractor to simultaneously learn the consistency and complementarity features of node attributes and network topology, aiming to capture more accurate and comprehensive features. Secondly, a noisy version of the feature extractor is constructed as a fake feature generator to quickly generate more unseen fake features, aiming to simulate network noise and construct two game players. Finally, a novel adversarial auto-encoder network is designed, which integrates the auto-encoder network and the generative adversarial network into a single network, aiming to quickly learn robust node embedding and tolerate network noise. Experiments on four publicly available datasets and three analysis tasks demonstrate the advantages of our AEANE model. [ABSTRACT FROM AUTHOR]

This paper presents the spectral gap-based topology control algorithm (SGTC) for wireless backhaul networks, a novel approach that employs the Laplacian Spectral Gap (LSG) to find expander-like graphs that optimize the topology of the network in terms of robustness, diameter, energy cost, and network entropy. The latter measures the network's ability to promote seamless traffic offloading from the Macro Base Stations to smaller cells by providing a high diversity of shortest paths connecting all the stations. Given the practical constraints imposed by cellular technologies, the proposed algorithm uses simulated annealing to search for feasible network topologies with a large LSG. Then, it computes the Pareto front of the set of feasible solutions found during the annealing process when considering robustness, diameter, and entropy as objective functions. The algorithm's result is the Pareto efficient solution that minimizes energy cost. A set of experimental results shows that by optimizing the LSG, the proposed algorithm simultaneously optimizes the set of desirable topological properties mentioned above. The results also revealed that generating networks with good spectral expansion is possible even under the restrictions imposed by current wireless technologies. This is a desirable feature because these networks have strong connectivity properties even if they do not have a large number of links. [ABSTRACT FROM AUTHOR]

P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered to adapt to the dynamic requirements of user request services and network topology. In order to address the challenges produced by these factors, an adaptive routing in P2P-based Edge Cloud is proposed, which is named ARPEC. In our approach, a target edge node selection scheme based on message activity and network topology is proposed, aiming to minimize the load on edge node and user access latency. Furthermore, to minimize system overhead, sensor data routing is mapped to minimum cost maximum flow (MCMF) graph. On this basis, a target edge node selection algorithm based on a grey linear regression combination prediction model is designed, and an incremental MCMF algorithm based on belief propagation (BP) is proposed. The evaluation results show that our approach can effectively improve PEC transmission performance and user experience. [ABSTRACT FROM AUTHOR]

We investigate the conformational properties of "ideal" nanogel particles having a lattice network topology by molecular dynamics simulations to quantify the influence of polymer topology on the solution properties of this type of branched molecular architecture. In particular, we calculate the mass scaling of the radius of gyration (Rg), the hydrodynamic radius, as well as the intrinsic viscosity with the variation of the degree of branching, the length of the chains between the branched points, and the average mesh size within these nanogel particles under good solvent conditions. We find competing trends between the molecular characteristics, where an increase in mesh size or degree of branching results in the emergence of particle-like characteristics, while an increase in the chain length enhances linear polymer-like characteristics. This crossover between these limiting behaviors is also apparent in our calculation of the form factor, P(q), for these structures. Specifically, a primary scattering peak emerges, characterizing the overall nanogel particle size. Moreover, a distinct power-law regime emerges in P(q) at length scales larger than the chain size but smaller than Rg of the nanogel particle, and the Rg mass scaling exponent progressively approaches zero as the mesh size increases, the same scaling as for an infinite network of Gaussian chains. The "fuzzy sphere" model does not capture this feature, and we propose an extension to this popular model. These structural features become more pronounced for values of molecular parameters that enhance the localization of the branching segments within the nanogel particle. [ABSTRACT FROM AUTHOR]

Whether and how the spatial arrangement of a population influences adaptive evolution has puzzled evolutionary biologists. Theoretical models make conflicting predictions about the probability that a beneficial mutation will become fixed in a population for certain topologies like stars, in which "leaf" populations are connected through a central "hub." To date, these predictions have not been evaluated under realistic experimental conditions. Here, we test the prediction that topology can change the dynamics of fixation both in vitro and in silico by tracking the frequency of a beneficial mutant under positive selection as it spreads through networks of different topologies. Our results provide empirical support that meta-population topology can increase the likelihood that a beneficial mutation spreads, broaden the conditions under which this phenomenon is thought to occur, and points the way toward using network topology to amplify the effects of weakly favored mutations under directed evolution in industrial applications. Lay Summary: Most natural populations are spatially structured, meaning that they are geographically subdivided and connected by migration. Whether spatial structure impacts adaptive evolution has been less clear as different theoretical approaches to modeling the spread of beneficial mutations through spatially structured populations make contrasting predictions. Our work uses evolution experiments and numerical simulations to show that spatial structure can impact the pace of adaptive evolution. Certain topologies, specifically a four-patch star with bidirectional migration through a central hub to each of three "leaf" populations, can accelerate the rate at which a beneficial mutation spreads through a population relative to an unstructured, well-mixed population. The cause of this acceleration is a reduced probability that beneficial mutations are stochastically lost when rare because they get concentrated in the central hub and then disperse outwards to the leaves. Our results offer the first experimental support for models of adaptive evolution in space based on evolutionary graph theory, may help understand the spread of invasive species or pathogens, and could be used in industrial settings to selectively enrich desired traits or biomolecules of interest. [ABSTRACT FROM AUTHOR]

The attribute network not only has a complex topology, but its nodes also contain rich attribute information. Attribute network embedding methods extract both network topology and node attribute information to learn low-dimensional embedding of large attribute networks, which are of great importance for network analysis. In this paper, we propose attribute network joint embedding based on global attention (GAJE). First, GAJE uses the structure information of the attribute network to obtain the structure embedding vectors of nodes. Second, we propose a global attention method to obtain the attribute embedding vectors of nodes. This method captures the relationships of different attributes within and between nodes through convolutional neural networks and attention mechanisms, respectively. Finally, GAJE concatenates the structure embedding vectors and the attribute embedding vectors to obtain the final joint embedding vectors which simultaneously reflects the network structure and attributes information. For link prediction and node classification, we compared GAJE with nine well-known network embedding methods in four real datasets. The experimental results show that the algorithm has good attribute network embedding effects. Our tensorflow implementation of the GAJE is available at https://github.com/Andrewsama/GAJE-master. • The network structure and the node attribute are extracted simultaneously. • The global attention mechanism gets the correlation information between the nodes. • Joint training captures the interactions between attribute and structure embeddings. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNA) are known to function as competing endogenous RNA (ceRNA) in various cancers by regulating microRNAs (miRNA). However, in colorectal cancer (CRC), the precise pathological role of circ000240/miRNA/mRNA remains indeterminate. The expression level of hsa_circ_000240 was evaluated using qRT-PCR in matching pairs of CRC tumor and adjacent normal tissue samples in our laboratory. Then, to determine whether hsa_circ_000240 acted as a ceRNA in CRC, the linked miRNAs and gene targets were retrieved. Topological analysis of candidate genes using a network approach identified the most critical hub genes and subnetworks related to CRC disease. Microarray and bulk RNA sequencing analyses were utilized to comprehensively evaluate the expression levels of both miRNA and mRNA in CRC. Single-cell RNA-seq analysis was also used to evaluate the significant overall survival (OS) genes at the cellular level. ATAC-seq data provided insights into candidate genes' accessible chromatin regions. The research uncovered a considerable upregulation of hsa_circ_000240 in CRC tissues. Three miRNAs interacted with the target circRNA. One thousand six hundred eighty intersected genes regulated by three miRNAs were further identified, and the relevant functionality of identified neighbor genes highlighted their relevance to cancer. The topological analysis of the constructed network has identified 33 hub genes with notably high expression in CRC. Among these genes, eight, including CHEK1, CDC6, FANCI, GINS2, MAD2L1, ORC1, RACGAP1, and SMC4, have demonstrated a significant impact on overall survival. The utilization of single-cell RNA sequencing unequivocally corroborated the augmented expression levels of CDC6 and ORC1 in individuals with CRC, alongside their noteworthy connection with the infiltration of immune cells. ATAC-seq analyses revealed altered accessibility regions in Chr2, 4, and 12 for CDC6 and ORC1 high-expression. Correlation analysis of CDC6 and ORC1 further highlighted the association of candidate gene expression with exhaustion markers such as CTLA4, CD247, TIGIT, and CD244. The candidate genes exhibit a positive correlation with chromatin remodeling and histone acetylation. These epigenetic modifications play a significant role in influencing the cancer progression following expression of CDC6 and ORC1 in CRC. Additionally, results showed that the methylation rate of the promoter region of CDC6 was elevated in CRC disease, confirming the functional importance of CDC6 and their interaction with hsa_circ_000240 and associated ceRNA in CRC. In conclusion, this study highlights hsa_circ_000240's role as a ceRNA in CRC. It opens new avenues for further dissection of CDC6, ORC1, and underlying novel epigenetics and immunotherapy targets for CRC therapy. [ABSTRACT FROM AUTHOR]

Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including Wireless Sensor Networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new localization approaches that combine DV-Hop with Chicken Swarm Optimization (CSO). The primary objective is to improve the precision of DV-Hop-based approaches. In this paper, we compare the efficiency of the proposed localization algorithms with other existing approaches, including several algorithms based on Particle Swarm Optimization (PSO), while considering random network topologies. The simulation results validate the efficiency of our proposed algorithms. The proposed HW-DV-HopCSO algorithm achieves a considerable improvement in positioning accuracy compared to those of existing models. [ABSTRACT FROM AUTHOR]

Within the conventional statistical physics framework, we study critical phenomena in configuration network models with hidden variables controlling links between pairs of nodes. We obtain analytical expressions for the average node degree, the expected number of edges in the graph, and the Landau and Helmholtz free energies. We demonstrate that the network's temperature controls the average node degree in the whole network. We also show that phase transition in an asymptotically sparse network leads to fundamental structural changes in the network topology. Below the critical temperature, the graph is completely disconnected; above the critical temperature, the graph becomes connected, and a giant component appears. Increasing temperature changes the degree distribution from power-degree for lower temperatures to a Poisson-like distribution for high temperatures. Our findings suggest that temperature might be an inalienable property of real networks. [ABSTRACT FROM AUTHOR]

With the development of network science, complex network analysis has received extensive attention. In recent years, influential spreaders identification has become a hot topic in the research of complex networks. In general, influential spreader identification algorithms are mainly divided into centrality-based algorithms and topology-based algorithms. However, centrality-based algorithms have to face the information limitation problem that leads to low accuracy for identifying influential spreaders. Topology-based algorithms have both structural and positional limitation problems that lead to low accuracy for identifying peripheral influential spreaders. In this paper, we focus on improving the situation and propose two influential spreader identification algorithms, NSC (neighborhood structure centrality) and NPC (neighborhood position centrality) from both the perspective of the centrality and the network topology. NSC algorithm collects various types of network structure information through structure embedding and clustering, so as to solve missing network structure information problem. NPC algorithm calculates neighborhood location information by improving the k-shell algorithm to tackle location limitation problem. Experimental results with fourteen baseline algorithms show that our proposed algorithms NSC and NPC can achieve higher accuracy. [ABSTRACT FROM AUTHOR]

Botnet attacks have mainly targeted computers in the past, which is a fundamental cybersecurity problem. Due to the booming of Internet of things (IoT) devices, an increasing number of botnet attacks are now targeting IoT devices. Researchers have proposed several mechanisms to avoid botnet attacks, such as identification by communication patterns or network topology and defence by DNS blacklisting. A popular direction for botnet detection currently relies on the specific topological characteristics of botnets and uses machine learning models. However, it relies on network experts' domain knowledge for feature engineering. Recently, neural networks have shown the capability of representation learning. This paper proposes a new approach to extracting graph features via graph neural networks. To capture the particular topology of the botnet, we transform the network traffic into graphs and train a graph neural network to extract features. In our evaluations, we use graph embedding features to train six machine learning models and compare them with the performance of traditional graph features in identifying botnet nodes. The experimental results show that botnet traffic detection is still challenging even with neural networks. We should consider the impact of data, features, and algorithms for an accurate and robust solution. [ABSTRACT FROM AUTHOR]

The topology identification of low-voltage distribution networks is an important foundation for the intelligence of low-voltage distribution networks. Its accuracy fundamentally determines the effectiveness of functions such as power system state estimation, operational control, optimization planning, and intelligent electricity consumption. The low-voltage distribution network is composed of transformers, lines, and end users. The key task of topology identification is to distinguish the connection relationship between distribution transformers, low-voltage lines, and phase sequence with end users, which can be divided into transformer user relationship, line user relationship, and phase user relationship. At present, the main methods of low-voltage network topology identification can be divided into signal injection method and data analysis method. The signal injection method requires a large number of additional terminal devices and is difficult to promote. The data analysis method combines the characteristics of switch state, voltage, current, electrical energy, and other data to perform topology analysis. The commonly used methods include correlation analysis and feature learning. Finally, typical problems that urgently need to be solved in topology recognition and representation were proposed, providing a reference for the research and development of low-voltage distribution network topology automatic recognition technology. [ABSTRACT FROM AUTHOR]

Triply period minimal surface (TPMS)-based porous microchannels with freeform surfaces are extensively used in various applications, e.g., bone scaffold design and thermal management. However, TPMS-based porous microchannels designed by most existing solutions are difficult to conform with the boundaries of freeform surfaces, and the integrity of the TPMS unit at the surface boundary is easily destroyed. Therefore, this work proposes a conformal design method for TPMS-based microchannels based on mesh surface conformal parameterization. A novel geometric structure, namely "quasi-quadrilateral," is presented with this approach to control the size and shape of TPMS unit. Then, a design method of TPMS network topology in the 2D parametric domain of mesh surfaces is proposed to determine the positions of TPMS units. Based on this network topology, an algorithm to generate conformal TPMS units and TPMS-based microchannels is further presented. The result microchannels can automatically adapt to various freeform surfaces, and the quality of TPMS unit is greatly improved. Moreover, the effectiveness and practicability of the proposed approach are validated by comparative experimental studies with existing solutions. [ABSTRACT FROM AUTHOR]

This article is devoted to the intellectual processing of text information for the purpose of detecting rail news. To solve the given task, the use of deep graph neural networks is proposed. Fake news detection based on user preferences is augmented with deeper graph neural network topologies, including Hierarchical Graph Pooling with Structure Learning, to improve the graph convolution operation and capture richer contextual relationships in news graphs. The paper presents the possibilities of extending the framework of fake news detection based on user preferences using deep graph neural networks to improve fake news recognition. Evaluation on the FakeNewsNet dataset (a subset of Gossipcop) using the PyTorch Geometric and PyTorch Lightning frameworks demonstrates that the developed deep graph neural network model achieves 94% accuracy in fake news classification. The results show that deeper graph neural networks with integrated text and graph features offer promising options for reliable and accurate fake news detection, paving the way for improved information quality in social networks and beyond. [ABSTRACT FROM AUTHOR]

When solving the problem of influence maximization (IM) in social networks accompanied by diffusion cascades, existing related methods face some problems, such as relying only on diffusion cascades while ignoring network topology, inaccurate estimation of node influence, etc. To tackle these problems, a novel method named HGIM is proposed from the perspective of heterogeneous graph. HGIM is consisted of a learning node representation process and a selecting influential nodes process. A heterogeneous propagation graph is first constructed to integrate network topology and diffusion cascades, where topological edges come from the social network, and action edges come from diffusion cascades. Moreover, an influence learning framework is constructed to learn node representation and estimate node influence. To select influential nodes, two seed selection strategies are proposed based on the estimated node influence. To evaluate the proposed method, a series of experiments are carried out on four real datasets. Experimental results confirm that the proposed method outperforms other state-of-the-art methods in solving the IM problem accompanied by diffusion cascades. [ABSTRACT FROM AUTHOR]

The real‐time performance is a major concern of complex real‐time systems during information acquisition, transmission, processing, and application. The interactions among information within these systems constitute complicated information exchange networks. Network topology plays an important role in the behaviors of information interactions and interferes, which severely affects the real‐time performance of the whole systems. Methods such as synchronization‐based method or traffic balancing strategy are the main ideas that guide the construction of network topology. However, these methods could not consider the real‐time performance in the process of topology construction. This article proposes an automatic topology generation algorithm with real‐time guarantee. Specifically speaking, the nodes in the network are clustered to generate the network topology by introducing the concepts of node degree and eigenvector centrality to ensure the real‐time performance of the network. The position of the nodes in the network is determined by comparing the node degree and the communication factors. Analytic method and simulation method are used to verify the real‐time performance of the proposed algorithm. Results show that the real‐time performance of more than half of the total information exchanges is improved compared with the traffic balancing method and topology grouping method for an industrial‐scale networking case. [ABSTRACT FROM AUTHOR]

The availability of industrial network topologies is limited, which presents a significant challenge for researchers in this field. Network generators are essential tools for generating realistic simulation outcomes in network research. Thus, it is crucial to use realistic topologies that can accurately represent the characteristics of the target network. The primary objective of this study is to address the existing research gap by developing an industrial network topology generator for novel topology datasets specific to industrial networks and the industrial Internet of Things (IIoT). In this paper, we describe the development of the graph theoretic ind ustrial topology gen erator (GENIND), a topology generator that can produce realistic IIoT and industrial network topologies. The proposed GENIND network topology generator is validated through comparisons with real-world industrial network topologies. The evaluation framework assesses the performance of the proposed GENIND system using three main measures: the overall network structure, path-related factors, and resilience topological structure features. The results of the evaluation demonstrate a high degree of similarity between the graphs generated by the GENIND system and the real-world industrial networks used for comparison. This finding suggests that the generated graphs can be utilized as reliable tools to predict the behavior of industrial networks under various conditions. [ABSTRACT FROM AUTHOR]

The inherent characteristics and limitations of Internet of Things networks make it hard to avoid facing adverse network conditions. Addressing high performance in extreme situations still remains a challenge even for a standardized routing protocol like the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). No effective support is provided by the design of RPL to guarantee high network performance in the presence of such challenging conditions. To address such a compelling need, an innovative approach referred to as Dynamic-RPL is proposed in this research paper. With only limited in-protocol modifications to RPL, Dynamic-RPL provides effective support of dynamic topology management in a distributed manner. Seamless optimization of network topology is realized with dynamic topological adjustments to sustain high network performance and stability. It incorporates modified RPL topology establishment, customized RPL objective function and parent selection, a new dynamic topology management algorithm, and additional inter-routing support. The evaluation results demonstrated the ability of Dynamic-RPL to maintain high overall network performance irrespective of the adversity of ongoing network conditions. Considering varying-scale experimental setups, high QoS performance and low energy consumption were achieved without much increase in network overhead. Dynamic-RPL succeeded in adapting responsively with little time required to have the network performance successfully restored and network topology completely converged. [ABSTRACT FROM AUTHOR]

Motivated by the modeling of customer mobility and congestion in supermarkets, we study queueing networks with a single source and a single sink. We assume that walkers traverse a network according to an unbiased random walk, and we analyze how network topology affects the total mean queue size Q, which we use to measure congestion. We examine network topologies that minimize Q and provide proofs of optimality for some cases and numerical evidence of optimality for others. Finally, we present greedy algorithms that add edges to and delete edges from a network to reduce Q, and we apply these algorithms to a network that we construct using a supermarket store layout. We find that these greedy algorithms, which typically tend to add edges to the sink node, are able to significantly reduce Q. Our work helps improve understanding of how to design networks with low congestion and how to amend networks to reduce congestion. [ABSTRACT FROM AUTHOR]

Recent supercomputers embody hundreds of thousands of compute nodes, and sometimes millions; as such, they are massively parallel systems. Node interconnection is thus critical to maximise the computing performance, and the torus topology has come out as a popular solution to this crucial issue. This is the case, for example, for the interconnection network of the Fujitsu Fugaku, which was ranked world no. 1 until May 2022 and is the world no. 2 at the time of the writing of this article. Here, the number of dimensions used by the network topology of such torus-based interconnects stays rather low: it is equal to three for the Fujitsu Fugaku's interconnect. As a result, it is necessary to greatly increase the arity of the underlying torus topology to be able to connect the numerous compute nodes involved, and this is eventually at the cost of a higher network diameter. Aiming at avoiding such a dramatic diameter rise, topologies can also combine several layers: such interconnects are called hierarchical interconnection networks (HIN). We propose, in this paper, which extends an earlier study, a novel interconnect topology for massively parallel systems, torus-connected toroids (TCT), whose advantage compared to existing topologies is that while it retains the torus topology for its desirable properties, the TCT network topology combines it with an additional layer, toroids, in order to significantly lower the network diameter. We both theoretically and empirically evaluate our proposal and quantitatively compare it to conventional approaches, which the TCT topology is shown to supersede. [ABSTRACT FROM AUTHOR]

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Background: It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. Objectives: We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy. Methods: Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts. Results: We included 41 patients (21 men), with a mean age of 60.1 years (range 23–82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p 1–30Hz = 0.002, p γ = 0.002, p β = 0.002, p α = 0.002, p θ = 0.024, and p δ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p 1–30Hz = 0.031, p δ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (p θ = 0.048) and decrease in WB node degree (p α = 0.039) in PSEs versus PNSEs at the uncorrected level. Conclusion: Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings. [ABSTRACT FROM AUTHOR]