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The problem of reduced-order H∞ filters design for Markovian jumping complex networks with polytopic time-varying transition probability matrices is first addressed in this paper, where the dynamic of each node is described by the sector-bounded nonlinearity. For the measurements, both quantisation and packet dropouts are considered, where each node has its own packet dropout rate. By using the mode- and transition probability-dependent Lyapunov function approach, two sufficient conditions are provided to ensure the stochastic stability and the disturbance attenuation performance of the resulting filtering error system. Then, the mode-independent reduced-order filters design method is proposed, and the filter parameters are given explicitly by linear matrix inequality method. Finally, the effectiveness of the theoretic results presented is illustrated via a numerical example which contains performance comparison of different mode-independent reduced-order filters. [ABSTRACT FROM AUTHOR]

With the rapid development of EV in the transportation system, its increasing influence on the power system has attracted more and more attention in recent years. Under such a background, a virtual EV energy network (VEEN) in traffic level coupled with the corresponding power grid is formed in this paper, considering the energy carried by EVs in a specified area as a virtual node. To further identify the vulnerable components in the coupled energy network, a hybrid approach based on a complex network (CN) is presented. A hybrid power transfer distribution factor (HPTDF) is proposed for the coupled energy network considering the variation of VEENs over time. Then, the line betweenness and the modified time-varying coupling net-ability are designed to evaluate the vulnerability of the coupled network. Furthermore, to demonstrate the applicability of the proposed models, three case studies are performed on the 39-bus IEEE power network coupled with a 19-node VEEN. The simulation result verifies the effectiveness of the method in terms of identifying the critical lines in a coupled energy network, and a vulnerability assessment progress by designing a slow-fast dynamic cascade failure simulator in this paper verifies that a VEEN layer coupling defense strategy can decrease the vulnerability of a power grid layer under random attacks. [ABSTRACT FROM AUTHOR]

Under the development trend of complexity and systematization of metro construction, there is an increasing number of risk factors potentially affecting construction safety, which has led to frequent accidents in metro construction projects, and the road to high-quality and sustainable development of metro construction is full of challenges. One of the essential reasons is that the propagation mechanism of safety risk factors in metro construction under hidden and delayed effects is not yet clear. This paper combines the theory of complex network and propagation dynamics and constructs a subway construction safety risk propagation model based on considering the hidden and delayed characteristics of construction safety risk propagation, which reveals the dynamic propagation law of subway construction safety risk and puts forward feasible coping strategies. The findings evince that the delay time T significantly affects the propagation behavior of risk and the achievement of the equilibrium state in the network. The transmissibility of the risk factor within the hidden state holds a pivotal sway over the entirety of risk propagation, and the latency in transmission significantly expedites the propagation of risk throughout the network. It is recommended that project managers monitor and warn safety state nodes and hidden state nodes to block the spread of risk in the network and control the delay time of risk in the network in time to reduce the probability of risk occurrence. This study significantly promotes the resilient management of safety risks in metro construction. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid framework for port container throughput forecasting, which is essential in global trade and transportation systems. It uses a multidisciplinary method that combines artificial intelligence, link prediction, and complex networks. To better grasp the interconnection and dynamics of port operations, time series data are first transformed using complex network theory into a network structure. The framework applies 13 similarity metrics, encompassing various aspects of network structural similarity, to form a feature set representing the complex port operation network. The most effective features are selected using the maximum relevance minimum redundancy (mRMR) method, adhering to systems theory's efficiency principles. These features are processed through SVM, DNN, and LSTM models for link prediction, which is crucial for forecasting in port logistics. Finally, the methodology concludes with regression analysis to obtain container throughput forecasts, which is a key metric in port systems management. Case studies of Shanghai Port and Shenzhen Port validate the framework's effectiveness, demonstrating a significant improvement in forecasting accuracy over the baseline models. This study contributes to systems analysis by showcasing a hybrid, AI-enhanced approach for managing and forecasting critical aspects of maritime trade systems. [ABSTRACT FROM AUTHOR]

Link prediction is an important task in social network analysis, with the goal of evaluating the possibility of forming links between node pairs. In this paper, we introduce compressed sensing technology into the link prediction task and propose a new link prediction method. Here, we transform the link prediction problem into a signal processing process. The idea is to take the links observed in social networks as the encoding result of compressed sensing signals, and then decode the encoded signal using the reconstruction algorithm to obtain more complete network information. We propose a sparse representation algorithm based on K-Singular Value Decomposition (K-SVD), which can integrate network topologies and node attribute information. On this basis, we construct the measurement matrix conforming to the Restricted Isometry Property (RIP), and further develop the Orthogonal Matching Pursuit (OMP) algorithm to handle the reconstruction issues. Experimental results on ten different types of real social networks show that the proposed method outperforms other state-of-the-art link prediction algorithms in terms of both accuracy and universality. In particular, it still performs stably when the network is sparse. [ABSTRACT FROM AUTHOR]

Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations. [ABSTRACT FROM AUTHOR]

The network representation learning task of fusing node multi-dimensional classification information aims to effectively combine node multi-dimensional classification information and network structure information for representation learning, thereby improving the performance of network representation. However, the existing methods only consider multi-dimensional classification information as priori features, which assists the representation learning of the network structure information, lacks the coping mechanism in the case of missing data, and have low robustness in the case of incomplete information. To address these issues, in this paper, we propose a representation learning model based on stochastic perturbation and homophily constraint, called IMCIN. On the one hand, the data transformation is carried out through the random perturbation strategy to improve the adaptability of the model to incomplete information. On the other hand, in the process of learning fusion representation vectors, an attribute similarity retention method based on the principle of homogeneity is designed to further mine the effective semantic information in the incomplete information. Experiments show that our method can effectively deal with the problem of incomplete information and improve the performance of node classification and link prediction tasks. [ABSTRACT FROM AUTHOR]

The systematicness of banks is an important driver of financial crisis. Overlapping portfolios and assets correlation of banks' investment are important reasons for systemic risk contagion. The existing systemic risk models are all analyzed from one aspect and cannot reflect the real situation of the banking system. In the present paper, considering the overlapping portfolios and assets correlation, a contagion network model with multi-channel risk is proposed, which is with interbank lending (direct contagion channel), overlapping portfolios (indirect contagion channel), and assets correlation (indirect contagion channel). In addition, the model takes investment risk as an impact factor and learns the operation rules of the banking system to help banks compensate for liquidity through asset depreciation. Based on the proposed model, the effects of assets correlation, assets diversity, assets investment strategy, interbank network structure, and the impact of market density on risk contagion are studied and analyzed quantitatively. The method in this paper can more truly reflect the banking system risk than the existing model. This paper provides a solution for quantitative analysis of systemic risk, which provides powerful tools for macroprudential stress testing and a reference for regulatory authorities to prevent systemic risk. [ABSTRACT FROM AUTHOR]

In the complex network of chemical process systems, if a node fails, it may trigger cascading failures and affect normal operation. To enhance the ability of chemical process systems to maintain normal operation after the cascading failure, this paper presents cascading failure modelling and robustness analysis of chemical process systems based on the complex network non‐linear load capacity model. First, based on complex network theory, a complex network model of the chemical process is constructed; then, three cascading failure models are constructed using a combination of linear and non‐linear load capacity models and initial load and initial residual capacity redistribution strategies; and finally, the nodes with the maximum node degree are deliberately attacked to analyze the robustness of the chemical process system in response to cascading failure. The case study shows that the proposed models are valid and feasible, and the robustness of the chemical process system is enhanced as the load and capacity parameters are increased. By reasonably setting the initial load and adjusting the model parameters, the robustness can be effectively improved, providing a theoretical reference for improving the robustness of the actual chemical process system in response to cascading failure. [ABSTRACT FROM AUTHOR]

The structural similarity based link prediction algorithms mainly exploit the information of network topology, such as links and nodes, to predict the potential links in complex networks. Among these algorithms, the local information similarity based algorithms have attracted the extensive attentions from the majority of researchers due to their low complexity and general applicability. The algorithms mainly exploit the attributes of common neighbors on the second-order transmission paths to predict the connection probability between the unconnected nodes, but ignore the structure attributes of endpoints. The structure attributes of an endpoint can be quantified as its influence resources, which make an important contribution to link prediction. To heighten the performances of local information based algorithms, this paper exploits the different structure attributes of endpoints to express the influence resources, and explores the contributions of the different endpoint attributes to local information algorithms. Extensive simulations on 12 real benchmark datasets show that, in most cases, the node degree expressing the influence resource makes the greatest contribution to improve the performances of the local information algorithms. Specifically, DCN, DAA and DRA algorithm possess the best prediction performances in 9, 5 and 7 datasets, respectively. Furthermore, compared with 6 mainstream algorithms, DRA as the best improved algorithm shows the optimal prediction performances in 8 datasets. [ABSTRACT FROM AUTHOR]

This paper examined the use of navigation and point-of-interests big data in three urban planning tools: community detection, road importance assessment, and urban function pattern recognition. We revealed the community structure for the urban spatial organization, followed by identifying major transit corridors and the urban function pattern. We found that the detected communities were significantly associated with the administrative divisions in the city. Major commercial and residential centers were primarily located across several communities. Besides, the spatial mismatch between commercial-residential areas and industrial development areas was also identified and examined for spatial structure optimization in urban development. [ABSTRACT FROM AUTHOR]

Objects with time factor are important representations of data, such as time series, sequences generated by chaotic systems, and dynamic networks. This paper focuses on characterizing the topological similarity of such objects. First, we introduce time-dependent object (TDO), which is time-dependent datasets with metric structures. Second, we extend the concept of topological correlation coefficient so that it can analyze time-dependent objects. The generalized topological correlation coefficient (GTCC) can be well defined on TDO and characterize the topological similarity between datasets. Third, we analyze examples constructed from chaotic systems and temporal networks. Calculations show that there are non-trivial topological similarities in both datasets, where surrogate TDOs provide benchmark values for comparison. In particular, GTCC can identify patterns in the temporal network arising from human behavior. The analytical framework presented in this paper has the potential to be widely used in time series analysis, nonlinear dynamical systems, and dynamic networks. [ABSTRACT FROM AUTHOR]

Multiple nodes (such as distributed generation (DG), electric vehicles (EV), energy storage (ES), flexible loads (FL), etc.) are connected to the active distribution network (ADN), which changes its original operational mode. According to the bidirectional current and low-voltage transmission mode, this study proposed a multi voltage and multi electricity flat loop network, AC/DC (Alternating Current/ Direct Current) hybrid network, unified interface and flexible self-organizing network based on Complex network theory. First, the ADN complex network topology of various nodes is established based on the actual grid connected terminal nodes and power flow sensitivity algorithm. Second, using the TOPSIS model, the influence factor matrix of weighted directed network is established. The matrix can be used to guide the formulation of the distribution network operation mode, and the robustness and reliability of this paper are verified by using the standard multi voltage level main distribution hybrid model provided by the Panda Power website as the verification method. Finally, using the influence maximization calculation model of the New Creedy algorithm, the node correlation matrix is expanded to form a super family region set of active distribution network. The results show that the seven nodes in this paper have high correlation, while the other nodes have low correlation. In addition, the change of reactive power has little impact on other nodes, for a node with a change rate of 0, it is obviously not in the same power supply family as node 1, and theoretically it may not have a topological relationship, be a power generation node, or be completely independent. Analyzing the relationship between nodes has a guiding significance for power supply recovery and interaction in distribution network reconfiguration. [ABSTRACT FROM AUTHOR]

In megacities, Internet of Things (IoT) based smart transportation is an effective solution to dealing with traffic congestions. Resilience evaluation of traffic networks can help enhance the management of IoT-based smart transportation. Currently, most of the research work on resilience evaluation is devoted to a single type of traffic network. However, in reality, urban traffic networks are complex, and various types of traffic networks are often highly interrelated and need to be evaluated concurrently. To address the above problem, this paper proposed a method for resilience evaluation of urban bus-subway traffic networks for potential applications in IoT-based smart transportation in megacities. The essential idea behind the proposed method is to combine the urban bus network with the subway network and present a model of an urban bus-subway hybrid traffic network, rather than a single type of traffic network. In the proposed method, (1) an urban bus-subway hybrid traffic networks model is established; (2) four metrics such as the degree centrality and clustering coefficient are used to identify vital nodes in the hybrid traffic network; and (3) four indicators such as the network efficiency and sensitivity are used to analyze the network resilience. The effectiveness of the proposed method is verified by the application of urban bus-subway hybrid traffic networks in Beijing. The proposed method can be potentially applied to optimize the resource allocation of important nodes in an urban hybrid traffic network, which is of great interest for enhancing the management of IoT-based smart transportation in megacities. [ABSTRACT FROM AUTHOR]

Community detection based on spectral clustering has been proved effective. However, spectral clustering is more challenging due to two significant issues: the construction of a good similarity matrix and the automatic detection of the number of clusters. In our previous paper, we developed a new similarity matrix for undirected networks based on Coulomb's law. It uses local and global measures to identify the communities efficiently using a label propagation algorithm. Thus, this paper extends our previous work to spectral clustering, and a novel community detection algorithm called SC_CL is proposed. Specifically, by exploiting the spectrum of the normalized Laplacian based on Coulomb's matrix, the graph's vertices are first embedded into a low-dimensional vector space, then k-means clustering is performed on the projected vertices. Experiments on synthetic benchmarks and real network datasets show that spectral clustering with this new similarity matrix achieves significant accuracy over existing methods. Moreover, the results provide clear and meaningful visualization of graph embedding in 2D space using dimensionality reduction. [ABSTRACT FROM AUTHOR]

Prostatic cancer is a complex cancer that many factors contribute to. In clinical practice, statins and oral anti-diabetic drugs, is the main adjuvant treatment for prostatic cancer patients. In this paper, a kind of typical complex network – the sample specific network (SSN) was used to screen out differentially expressed genes (DEGs) between prostatic cancer and normal tissue. And by the degree of SSN, driving mutation genes (DMGs) were determined. Pearson correlation coefficient was used to construct SSN, in order to describe the special characteristics of the sample. In this way, people can realise personalised medical service. After the prediction of the degree top 30, top 20, top 10, and top 5 genes, the accuracy of prediction of driving genes with the top 5 genes is the highest. Different single samples of SSN from prostate cancer have specificity, but there is a common edge or network with the specific subnetwork of driving gene. The driving genes of prostate cancer can be predicted by our method. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme weather and national conflicts has highlighted the necessity of enhancing the robustness of the grain trade network. In this paper, we study the evolution of major trading countries, and the network robustness under random and different attack strategies. The results show that the heterogeneity of the Global Soybean Trade Network (GSTN) and trade connections among countries are increasing. The soybean trade network has strong resistance to random attacks, while subjected to targeted attacks, the stability of the network is significantly affected and damaged. [ABSTRACT FROM AUTHOR]

Container transportation has the advantages of standardization, high efficiency, and high safety, which are essential for promoting the development of the world economy and trade. Emergencies such as severe weather, public health incidents, and social security incidents can negatively affect the operational reliability of the container shipping network. To ensure the network security and high-quality operation of container shipping, a double-layer coupled container transportation network is first described to analyze the evolution of the container shipping network and the risk propagation dynamics of operation participants. On this basis, a cascade failure model of the container shipping network considering risk level is constructed. To evaluate the vulnerability of the container shipping network, the transmission mechanism of cascade failure effects of the container shipping network under different emergency development trends and the evolution law and influence path of the container shipping network structure are both analyzed. Finally, we empirically studied the container shipping network in China and the United States, and characteristic parameters of the China–U.S. container shipping network are calculated and analyzed. The model's validity is verified through practical cases and model simulation results, and the cascading failure process of the container shipping network in China and the United States under three types of attacks is simulated. Suggestions are provided for effective improvement in the vulnerability of the container shipping network under every kind of contingency. [ABSTRACT FROM AUTHOR]

This paper introduces a novel node centrality index based on edge dynamics in a complex network: adjacent edge index. Through the analysis of network dynamics and topology, we provide a neat index to qualify node importance, which is calculated by the eigenvector components corresponding to the Fiedler value of the network. Notably, it identifies nodes whose adjacent edge sets play a significant role in network synchronization. The index indicates that not only a node's degree but also its location should be considered. To demonstrate the effectiveness of the proposed index, consider a double-star network and a Jazz Musicians network in which chaotic Chua's circuits as nodes are coupled. By factoring in the dynamic information exchange between nodes and their neighbors, this index offers valuable insights for optimizing strategies in engineering areas such as efficient information dissemination. [ABSTRACT FROM AUTHOR]

This paper concerns the synchronization of a kind of drive-response multi-layer dynamical networks with additive couplings and stochastic perturbations. Multi-layer networks are a kind of complex networks with different layers, which consist of different kinds of interactions or multiple subnetworks. Additive couplings are designed to capture the different layered connections. In this paper, two pinning controllers are designed to guarantee the synchronization of the stochastic multi-layer network. One is the state-feedback pinning controller with constant control gains. The other one is the adaptive pinning controller with adaptive control gains. It is worthwhile to mention that our assumptions on the activation functions satisfy a generalized Lipschitzian condition which are weaker than those in the previous works. Moreover, as we prove, only selected part of the nodes to be controlled are enough to guarantee that the drive system and response network can be stochastically synchronized. Finally, an example and its simulations are presented to show the feasibility effectiveness of our control schemes. [ABSTRACT FROM AUTHOR]

In this paper, the finite-time and fixed-time stochastic synchronization of complex networks with pinning control are investigated. Considering the time and energy cost of control, combining the advantages of finite-time control technology and pinning control technology, efficient protocols are proposed. Compared with the existing research, the influence of noise is considered, and sufficient conditions for the network to achieve stochastic synchronization in a finite time are given in this paper. Based on the stability theory of stochastic differential equations, the upper bound of the setting time is estimated. Finally, the effects of control parameters, noise intensity, and the number of control agents on the network synchronization rate are studied. Numerical simulations verify the validity and correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper addresses the problem for output synchronization of a class of complex networks (CNs) with reaction-diffusion terms and input constraint. Without considering input constraint, proportional-spatial derivative (P-sD) control of the CN is firstly studied, and then, the problem of output synchronization of CN is formulated as the feasibility problem of linear matrix inequalities (LMIs). Based on the obtained results, considering P-sD control with input constraint, output synchronization is then studied and formulated as the feasibility problem of LMIs and spatial algebraic linear matrix inequalities (SALMIs). In the end, numerical simulation shows the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

We present the algebraic representation and basic algorithms for MultiAspect Graphs (MAGs). A MAG is a structure capable of representing multilayer and time-varying networks, as well as higher-order networks, while also having the property of being isomorphic to a directed graph. In particular, we show that, as a consequence of the properties associated with the MAG structure, a MAG can be represented in matrix form. Moreover, we also show that any possible MAG function (algorithm) can be obtained from this matrix-based representation. This is an important theoretical result since it paves the way for adapting well-known graph algorithms for application in MAGs. We present a set of basic MAG algorithms, constructed from well-known graph algorithms, such as degree computing, Breadth First Search (BFS), and Depth First Search (DFS). These algorithms adapted to the MAG context can be used as primitives for building other more sophisticated MAG algorithms. Therefore, such examples can be seen as guidelines on how to properly derive MAG algorithms from basic algorithms on directed graphs. We also make available Python implementations of all the algorithms presented in this paper. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic performance of subway transportation systems. A three-layer model, consisting of a rail layer, a train layer, and a passenger layer, is proposed to describe the structure and operation of a metro system. Two parameters, namely, time efficiency and maximum load ratio, are proposed to assess the transport efficiency and the train utilization rate, respectively. Case studies of the metro networks in Beijing, Tokyo, and Hong Kong show that the time efficiency decreases nonlinearly with the increase of vehicle resource and passenger demand, whereas maximum load ratio varies in an opposite manner. For the three metro networks under study, the Tokyo metro system has the highest time efficiency when passengers adopt a shortest-path (SP) routing strategy, while the Hong Kong system’s highest time efficiency exceeds the others’ when passengers adopt a minimum-transfer-path (MTP) strategy. In general, the maximum time efficiency is higher when passengers adopt SP routing rather than MTP routing. Moreover, the Beijing metro system has the highest maximum load ratio, regardless of the passengers’ routing behavior. This paper can be applied to a metro network to optimize the train departure interval under a certain passenger entrance rate, with the aim to maximize the time efficiency and maximum load ratio. Our model permits assessment of the operational effectiveness of metro systems, which helps the metro operators to improve the performance and reduce the cost. [ABSTRACT FROM AUTHOR]

The innovation and development of emerging technology mostly depend on the way of knowledge convergence defined as the blurring of previously distinct domain-specific knowledge. This paper aims to explore the potential motivation of knowledge convergence and find the law of knowledge convergence, taking the solar energy field as an example. We established Keywords co-occurrence networks of solar energy literature in 2008–2017, and then link prediction is introduced to study the structural mechanism of knowledge convergence. We found that: (1) the common neighbor index better characterizes the knowledge convergence pattern in the knowledge networks among four similarity indicators. (2) The keywords co-occurrence network could effectively mine the structural characteristics of knowledge convergence; (3) the convergence cycle of knowledge in the field of solar energy was about 4 years; (4) keywords with higher betweenness centrality or eigenvector centrality easily generated knowledge convergence; (5) a literature knowledge convergence prediction model is proposed based on these results; and (6) the prediction results showed that scholars should pay attention to six basic issues including energy storage, efficiency, cost, ecological effect, application scenarios, and hybrid photovoltaic systems. This work can provide guidance not only for scholars to grasp the research direction and to generate more innovations but for the government to formulate the policies of government funding. [ABSTRACT FROM AUTHOR]

Urban function renewal is essential for modern megacities' urban planning and economic developments. This paper investigates the urban function renewal in Shenzhen, China based on a complex network method. According to the points of interest and the location quotient, the dominant urban functions in each district are discussed. After computing conditional probability, the interdependence of urban functions is analyzed. The complex networks of the functions and the corresponding clusters are presented to examine the relationship and the overall features of the functions, and the features of the function clusters, respectively. The average degree and average weighted degree of the main function categories of the functions are computed to explore the features of the function classification. The urban functions' renewal potential index is calculated to show the potential of the non-dominant functions renewing to the dominant ones in the coming years. The difficulty index of the urban function renewal in each district is presented, and the difficulty degree of the original d-ominant function group renewing to a new one is obtained. The results show that more dominant urban functions have a significant probability of being dominant ones in a district; the functions of hotels and life services are essential in the planning and development in Shenzhen; and the districts with better economic levels have greater values of the difficulty of the urban function renewal. Then, the function renewal direction in Shenzhen is analyzed, and some policy implications are given. [ABSTRACT FROM AUTHOR]

With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China. [ABSTRACT FROM AUTHOR]

Entropy is widely applied to graph theory and complex networks as a powerful tool for measuring uncertainty in a complex system. Due to the fact that traditional probability distribution entropy cannot effectively characterize the global topology information of complex networks, some entropy measures constructed by the adjacency matrix A come into being, such as information-theoretic entropy EE and communicability sequence entropy. Despite substantial efforts to explore the properties of these measures, there remain some imperfections. For instance, the adjacency matrix only reflects the dependence between direct neighbors. Therefore, in this paper, we propose the r -power adjacency matrix entropy ( AME r ) to measure the indirect relationship between nodes in a network. And then, we compare the abilities of AME r , EE, and CSE in capturing the network global topology changes. Furthermore, we establish the Jenson–Shannon divergence based on AME r to quantify the structural dissimilarities of the networks. Finally, we apply the proposed methods to analyze the urban economic connection networks. The results demonstrate the availability of the proposed measures in identifying network topology changes and quantifying network structure differences. [ABSTRACT FROM AUTHOR]

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Analyzing the robustness of networks against random failures or malicious attacks is a critical research issue in network science, as it contributes to enhancing the robustness of beneficial networks and effectively dismantling harmful ones. Most studies commonly neglect the impact of the attack success rate (ASR) and assume that attacks on the network will always be successful. However, in real-world scenarios, attacks may not always succeed. This paper proposes a novel robustness measure called Robustness-ASR (RASR), which utilizes mathematical expectations to assess network robustness when considering the ASR of each node. To efficiently compute the RASR for large-scale networks, a parallel algorithm named PRQMC is presented, which leverages randomized quasi-Monte Carlo integration to approximate the RASR with a faster convergence rate. Additionally, a new attack strategy named HBnnsAGP is introduced to better assess the lower bound of network RASR. Finally, the experimental results on six representative real-world complex networks demonstrate the effectiveness of the proposed methods compared with the state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Link prediction aims to identify unknown or missing connections in a network. The methods based on network structure similarity, known for their simplicity and effectiveness, have garnered widespread attention. A core metric in these methods is "proximity", which measures the similarity or linking probability between two nodes. These methods generally operate under the assumption that node pairs with higher proximity are more likely to form new connections. However, the accuracy of existing node proximity-based link prediction algorithms requires improvement. To address this, this paper introduces a Link Prediction Algorithm Based on Weighted Local and Global Closeness (LGC). This algorithm integrates the clustering coefficient to enhance prediction accuracy. A significant advantage of LGC is its dual consideration of a network's local and global features, allowing for a more precise assessment of node similarity. In experiments conducted on ten real-world datasets, the proposed LGC algorithm outperformed eight traditional link prediction methods, showing notable improvements in key evaluation metrics, namely precision and AUC. [ABSTRACT FROM AUTHOR]

Detailed knowledge about the synaptic plasticity is the key for understanding of memory formation and its changes with aging and diseases. In this paper, we explain the mechanism of synaptic plasticity and link it to the memory formation. A chemical synapse exhibits the same hysteresis curve as a memristor within a presynaptic firing spike, and the memory line is determined by the nonlinear relation between the transmitter concentration and the presynaptic potential. The lower presynaptic voltage threshold of transmitter releasing induces the loss of such memory effect in the synapse. In addition, the link is investigated between the plasticity and memory basing on three aspects, including synaptic plasticity for a given memory (necessity), suppression of synaptic plasticity after acquiring the memory (saturation) and forgotten events (erasure or inaccessible states). Moreover, the small-world brain network and scale-free brain network exhibit desynchronization behaviors with the memory effect, and they show the robustness of some diseases. These results shed light on the understanding of the synaptic plasticity and disclose the principle of memory formation. [ABSTRACT FROM AUTHOR]

Identifying influential nodes in social networks has drawn significant attention in the field of network science. However, most of the existing works request to know the complete structural information about networks, indeed, this information is usually sensitive, private and hard to obtain. Therefore, how to identify the influential nodes in networks without disclosing privacy is especially important. In this paper, we propose a privacy-preserving (named as HE-ranking) framework to identify influential nodes in networks based on homomorphic encryption (HE) protocol. The HE-ranking method collaboratively computes the nodes' importance and protects the sensitive information of each private network by using the HE protocol. Extensive experimental results indicate that the method can effectively identify the influential nodes in the original networks than the baseline methods which only use each private network to identify influential nodes. More importantly, the HE-ranking method can protect the privacy of each private network in different parts. [ABSTRACT FROM AUTHOR]

This paper is focused on the problem of finite-time projective synchronization (FTPS) of complex dynamical networks (CDNs) with or without time-varying coupling delays. An effective adaptive control strategy is designed to achieve finite-time projective synchronization. Utilizing the Lyapunov stability method and corresponding parametric inequality, two distinct controllers and adaptive laws are created for realizing the FTPS of CDNs with or without coupling delays. Through mathematical calculation and analysis, some necessary conditions for keeping FTPS are generated, and the convergence time of the network errors is evaluated concretely. Finally, the effectiveness of the proposed controller is verified by some numerical simulations. [ABSTRACT FROM AUTHOR]

Traffic state information, road network structure characteristics, and built environment characteristics are factors influencing traffic safety, which will alleviate or aggravate traffic safety problems. This paper analyzes the relationship between these factors and traffic accidents involving either property damage only (PDO) crashes or killed and severe injury (KSI) crashes. The spatiotemporal distribution of the two types of accidents was analyzed. Abundant explanatory variables were extracted from accident data, license plate recognition (LPR) data, OpenStreetMap (OSM) data, and point of interest (POI) data based on complex network methods and information entropy theories. Geographical and temporal weighted regression (GTWR), geographically weighted regression (GWR), and ordinary least squares (OLS) models were used to analyze the influencing factors of traffic safety, respectively. The results demonstrate that the GTWR model performs best in modeling spatiotemporal heterogeneity data. Traffic state information, road network structure, and built environment factors all have significant effects on traffic accidents, and traffic state information have the highest correlation with traffic accidents among all factors. The greater the traffic volume, the more likely are traffic accidents. The strongest correlation is between PDO crashes and traffic state in the morning peak, in the evening peak, and at night. For a road network divided into grids, the more important the intersections in the grid, the greater is the street circuity, and the more chaotic the street direction, the more likely PDO crashes are to occur in the grid. Furthermore, the diversity of land use is positively correlated with traffic accidents in urban areas, whereas the correlation is negative in suburban areas, which reflects the spatial heterogeneity. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of network conformation on chaos synchronization in driven complex networks in a master-slave setting. We analyze the control and prediction of complex networks under perturbations. Although chaotic dynamics suppression is well understood in low-dimensional systems, it is not well described in more complex ones. To assess a network's ability to amplify or suppress disturbances, we track ensembles of possible trajectories in the driven subsystem which give insights into nonlocal convergence properties in a master-slave setting. We demonstrate how the stability of forced networks of Rulkov neurons is dependent on network coupling architecture and how the duration of chaotic transients depends on initial conditions. Furthermore, we find that each network generator algorithm has specific sets of parameters that can lead to stable or nonstable states. Our results indicate that network types are more critical than isolated connections for suppressing chaotic dynamics in driven networks. Finally, we identify that under certain circumstances, the transient period before synchronization correlates with high convergence rates for certain initial conditions. This suggests nonuniform convergence rates in the phase space of driven neural networks. [ABSTRACT FROM AUTHOR]

A community is composed of closely related nodes. Detecting communities in a network has many practical applications, such as online product recommendation, biological molecule discovery and criminal group tracking. In recent years, network representation learning (NRL) has attracted much attention in the field of community detection because it can effectively extract complex relations between nodes which improves the quality of detected communities. In many real-world networks, the rich attribute information contained in nodes and the similarity between nodes and their multi-order neighbors has significant contributions to the generation of node embedding vectors in NRL. However, existing NRL algorithms treat a node's different order neighbors equally as high-order contexts, leading to the ignorance of their different impacts on the generation of the node's embedding vector. In addition, these algorithms do not focus on the coupling and interaction relations between nodes playing similar structural roles, which may ignore some nodes in a community with similar structural roles. In this paper, we propose a novel autoencoder considering the multi-order similarity and structural role similarity (AMOSOS) to solve the above problems. First, we design a strategy to obtain a multi-order weight matrix which preserves the differential influence of neighbors of different orders by sequentially decreasing the weight of each order. Second, we design a role similarity indicator to capture the complex coupling and interaction relations of nodes in the network. Experimental results on synthetic networks and real-world networks show that our proposed algorithm is more accurate than existing network representation learning algorithms for the task of community detection. [ABSTRACT FROM AUTHOR]

Facing the Bicycle Rebalancing Problem (BRP), we established a Rebalancing Incentive System (BRIS). In BRIS, the bicycle operator proposes the method of financial compensation to encourage cylclists to detour some specific stations where the number of bikes is excessive or insufficient and access suitable sations. BRIS mainly includes two objects: the Bike Gym imitating the bicycle environment, and the Spatiotemporal Rebalancing Pricing Algorithm (STRPA) determining the amount of money which is given to the cyclist depending on time. STRPA is a deep reinforcement learning model based on the actor–critic structure, which is the core concept of this paper. In STRPA, the hierarchical principle is introduced to solve the dimensional disaster, and the graph matrix A is introduced to solve the complex node relationship. In addition, the traffic data including the bicycle have strong temporal and spatial characteristics. The gated recurrent unit (GRU), the sub-module of STRPA, can extract the temporal characteristics well, and the graph convolution network (GCN), also a sub-module of STRPA, can extract the spatial characteristic. Finally, our model is superior to the baseline model when verified on the the public bicycle data of Nanjing. [ABSTRACT FROM AUTHOR]

Currently, there is a large amount of text being shared through the Internet. These texts are available in different forms—structured, unstructured and semi structured. There are different ways of analyzing texts, but domain experts usually divide this process in some steps such as pre-processing, feature extraction and a final step that could be classification, clustering, summarization, and keyword extraction, depending on the purpose over the text. For this processing, several approaches have been proposed in the literature based on variations of methods like artificial neural network and deep learning. In this paper, we conducted a systematic review of papers dealing with the use of complex networks approaches for the process of analyzing text. The main results showed that complex network topological properties, measures and modeling can capture and identify text structures concerning different purposes such as text analysis, classification, topic and keyword extraction, and summarization. We conclude that complex network topological properties provide promising strategies with respect of processing texts, considering their different aspects and structures. [ABSTRACT FROM AUTHOR]

During the rapid outbreak of the COVID-19 epidemic, the construction of the Thunder God Mountain Hospital (TGMH) in a harsh environment significantly lowered the burden of medical assistance in Wuhan, demonstrating the significance of organizational resilience in construction projects to handle harsh environments. This paper aims to explore the impact of organizational collaboration patterns on organizational resilience in construction projects and to outline the lessons that can be gained for similar projects. Firstly, an extensive literature review was conducted to determine organizational resilience indicators and corresponding complex network parameters, followed by a content analysis approach to identify the organizations involved and their collaboration behaviors. Secondly, the obtained organizations and collaboration data were used to construct an organization collaboration network for the TGMH project. The results of the complex network analysis show the following as critical factors affecting organizational resilience: (1) the one core multi-center organizational collaboration structure; (2) the small group relationships among organizations; and (3) assortative ties between organizations with diverse backgrounds. This study contributes to providing a feasible research framework for assessing organizational resilience from the perspective of organizational collaboration and practical suggestions for other construction projects to improve organizational resilience when faced with large public emergencies. [ABSTRACT FROM AUTHOR]

This paper analyzes the price strategy of electricity retailers in different trading scenarios. In our empirical work, we use the evolutionary game model based on the complex network to describe the information interaction relationship and game relationship between electricity retailers, and reflect the user's switching behavior through the market share function. The purpose of our work is to study the impact of network structure, contract transaction, user's switching behavior and strategy updating rules on the price strategy of electricity retailers by applying the evolutionary game model on complex network to the retail market. The results show that network scale, contract electricity, user switching degree and overconfidence level have important influence on the price strategy selection of electricity retailers, and these parameters should be maintained within a reasonable range in order to maximize the interests of electricity retailers and achieve the balance of retail bidding. By mapping the results of the game model to the actual policy and retail market, we believe that the evolutionary game model on network is a useful tool to analyze the competition of electricity retailers. More importantly, the conclusions can provide a reference for electricity retailers when choosing a retail price strategy and for future works which aim to analyze the development of the retail electricity market. [ABSTRACT FROM AUTHOR]