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Drug combinations have emerged as a promising therapeutic approach in cancer treatment, aimed at overcoming drug resistance and improving the efficacy of monotherapy regimens. However, identifying effective drug combinations has traditionally been time-consuming and often dependent on chance discoveries. Therefore, there is an urgent need to explore alternative strategies to support experimental research. In this study, we propose network-based prediction models to identify potential drug combinations for 11 types of cancer. Our approach involves extracting 55,299 associations from literature and constructing human protein interactomes for each cancer type. To predict drug combinations, we measure the proximity of drug-drug relationships within the network and employ a correlation clustering framework to detect functional communities. Finally, we identify 61,754 drug combinations. Furthermore, we analyze the network configurations specific to different cancer types and identify 30 key genes and 21 pathways. The performance of these models is subsequently assessed through in vitro assays, which exhibit a significant level of agreement. These findings represent a valuable contribution to the development of network-based drug combination design strategies, presenting potential solutions to overcome drug resistance and enhance cancer treatment outcomes. [ABSTRACT FROM AUTHOR]

We present a method based on graph community detection algorithms to analyse velocity fields induced by an intruder particle impinging upon a stationary bed of particles. Based on velocity relations between the pairs of adjacent particles, the “velocity similarity” graphs are built where the graph vertices represent the particles and the edge weights are calculated according to the velocities of the respective particle pairs. A few different expressions for the edge weights are tested. Based on the graph, a Louvain community detection algorithm with the “geographic” null model is used to identify the groups of particles moving in a coordinated manner, represented in the graph as a community of vertices, for which the community detection algorithms developed for graph analysis can be applied. Selection of the expression of the graph edge weights based on the velocities of the respective particles influences the resulting graph structure and thereby has an influence on the community detection results. [ABSTRACT FROM AUTHOR]

The Systems Engineering Research Center (SERC) is a University Affiliated Research Center (UARC) of the US Department of Defense (DoD) formed in 2008 with more than 20 collaborator universities in the United States. Over the last decade, SERC has conducted research with Principal Investigators from universities within the SERC network, as reflected in technical reports (TR). These reports describe detailed information and analysis of the conducted research for every project under SERC support, such as written records of experiments or results of a scientific project. We analyzed the TRs from 2009 to early 2023 to identify research streams, topics, and evolution in systems engineering (SE) research using text mining and network analysis techniques, such as Louvain Community Detection and word similarity. As a result, we identified four major research streams over a decade of research projects, along with insights about topics and the evolution of SE within this time frame. Finally, we distinguished most profile authors and their most significant collaborations and networks. [ABSTRACT FROM AUTHOR]

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time‐varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age‐related dynamic modular reconfiguration using resting‐state functional MRI data (615 participants, aged 18–88 years) from Cam‐CAN. We employed a graph‐based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age‐related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole‐brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory‐related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large‐scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions. [ABSTRACT FROM AUTHOR]

The task of a semantic community query is to obtain a subgraph S based on a given query vertex q (or vertex set) and other query parameters in an attributed graph G such that S belongs to G, contains q and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named SCQ in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, SCQ applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs. [ABSTRACT FROM AUTHOR]

Communities in a network are groups of nodes that are more strongly connected to each other. This article proposes a novel method for community detection in dynamic networks, focusing on influential nodes and overlapping communities. The method, named community detection based on adaptive multi-centrality aggregation (CDAMA), tackles two key challenges identifying influential nodes and overlapping communities. CDAMA introduces the Adaptive multi-centrality aggregation (AMCA) approach to identify influential nodes. AMCA integrates multiple centrality measures. The adaptive overlap control and merging (AOC-CM) approach addresses overlapping communities. AOC-CM utilizes structural, temporal, and semantic factors to strategically merge communities while preserving those with minimal overlap. CDAMA consists of five phases: receiving network snapshots, selecting influential nodes, launching communities, checking overlap and merging communities, and updating communities. Evaluation on three benchmark datasets demonstrates that CDAMA outperforms existing state-of-th-art methods in terms of Newman modularity, Modularity with split penalty and density modularity and Execution time. This suggests CDAMA is a valuable tool for tasks like viral marketing, information diffusion analysis, and network resilience studies. [ABSTRACT FROM AUTHOR]

The rapid development of the internet and social network platforms has given rise to a new field of research, social network analysis. This field of research has many fundamental problems, one of which is community detection. The objective of this research is to understand hidden connections among individuals. However, uncovering these connections are still challenging, despite the existence of several methods. In this paper, we propose a new algorithm called MCCD (Modified Cosine for Community Detection) for community detection in social networks based on node similarity. Our algorithm consists of two steps. In the first step, we use a novel cosine similarity formula to identify initial communities. In the second step, we merge these communities based on a new similarity measure. MCCD can be used in two different ways. The first way uses K as an input to identify the exact communities. The second way does not require K and aims to provide the best partitioning by maximizing modularity. Our algorithm has been tested on a variety of artificial and real-world networks, and the experimental results demonstrate its superiority over existing methods in detecting communities. [ABSTRACT FROM AUTHOR]

This study characterises the geography of Colombian exporters and their response to the COVID-19 crisis. We define exporting clusters within departments using bipartite network analysis and community detection tools. The methodology empirically detects product communities – groups of products that are strongly related – which are compared with an alternative taxonomy of industrial clusters, and clusters exporting firms by considering their exports within these taxonomies. We define these groups of specialised firms localised in departments as exporting clusters. We then examine whether belonging to an exporting cluster can enhance firms' trade margins. We find that clusters do not automatically improve firms' trade margins and that there are differences in how firms react to a crisis within clusters. Our analysis contributes to regional studies by shedding light on the potential of exporters' agglomerations to navigate and recover from crises and outlining critical firm characteristics that can strengthen regional resilience. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Community detection in social networks plays a crucial role in understanding various social phenomena. However, accurately identifying communities is challenging due to the complex nature of social networks. To address this challenge, we propose CD-BSO, an innovative metaheuristic based on brainstorming. CD-BSO leverages network knowledge through specialized initialization and solution generation operators designed to capture social network characteristics. In the initialization phase, a technique combining Depth-First Search (DFS) is employed to consider both connectivity and information diffusion, ensuring accurate community representation. The generation step introduces search operators that consider link formation and node similarity, facilitating convergence towards a community structure that closely resembles the network's actual structure. The evaluation of CD-BSO outperforms recent and well-known community detection methods. The evaluation results were further analyzed using visual analysis, providing valuable insights into the network structure. CD-BSO exhibits significant potential for accurately identifying communities and extracting meaningful information from social networks. [ABSTRACT FROM AUTHOR]

Community detection is a valuable tool for studying the function and dynamic structure of most real-world networks. Existing techniques either concentrate on the network's topological structure or node properties without adequately addressing the dynamic aspect. As a result, in this research, we present a unique technique called Multi-Objective Optimization Overlapping Dynamic Community Detection (MOOODCD) that leverages both the topological structure and node attributes of dynamic networks. By incorporating the Dirichlet distribution to control network dynamics, we formulate dynamic community detection as a non-negative matrix factorization problem. The block coordinate ascent method is used to estimate the latent elements of the model. Our experiments on artificial and real networks indicate that MOOODCD detects overlapping communities in dynamic networks with acceptable precision and scalability. [ABSTRACT FROM AUTHOR]

In real-world networks, the structure and connections are constantly evolving over time. Detecting community changes within dynamic networks has always been an important research topic. When such changes are significant, it leads to difficulty for community detection algorithms to effectively utilize valuable information from the previous network snapshot, resulting in negative transfer in the next time step. To address the issue of poor algorithm adaptability to network mutations, this paper proposed a dynamic community detection algorithm based on genetic evolution ideas and higher-order knowledge transfer strategies. Firstly, it used the adjacency matrix similarity of adjacent snapshots to determine the use of first-order or higherorder information. Then, it employed the spider Web model for population initialization, followed by the non-dominated sorting genetic algorithm NSGA-II to iteratively obtain multi-objective optimal solutions on the Pareto front. It designed a novel gene crossover method to enhance population diversity. Finally, experimental results on multiple real and simulated datasets demonstrate that, compared to existing algorithms, the proposed method achieves higher temporal smoothness in community detection results during network upheavals while maintaining a good community modularity level. [ABSTRACT FROM AUTHOR]

Multiplex network community detection methods based on graph neural network face two main challenges. Firstly, how to effectively utilize the node content information of multiplex network; and secondly, how to effectively utilize the interlayer relationships in multiplex networks. Therefore, this paper proposed the multiplex network community detection model AGCFN. Firstly, the autoencoder independently extracted the node content information of each network layer and passed the extracted node content information to the graph autoencoder for fusing the node content information of the current network layer with the topology information through the transfer operator to obtain the representation of each node of the current network layer, which made full use of the node content information of the network and the topology information of the network. The modularity maximization module and graph decoder optimized the obtained node representation. Secondly, the multilayer information fusion module fused the node representations extracted from each network layer to obtain a comprehensive representation of each node. Finally, the model under went training, and it achieved community detection results through a self-training mechanism. Comparison with six models on three datasets demonstrate improvements in both ACC and NMI evaluation metrics, thereby validating the effectiveness of AGCFN. [ABSTRACT FROM AUTHOR]

Existing regionalization methods tend to be either spatial attribute- or spatial interaction-based, while real-world tasks usually involve both considerations to satisfy multiple objectives simultaneously. In this research, we propose Multilayer Network Community Detection and Kernel Extension (MNCD-KE), a two-step regionalization framework, as a feasible solution for such tasks. First, spatial attributes are embedded into attributes of nodes in a spatial interaction-defined multilayer network, and the kernel and marginal parts of the regions are determined by giving the membership value of the regionalization units to network communities. Second, the final result is obtained through a kernel extension process considering geographical constraints, including spatial contiguity, size balance, morphological regularity, and existing boundary consistency of the regions. Empirical experiments show that the proposed method yields outcomes that, in maintaining comparable performances with most baseline algorithms with either 'attribute' or 'interaction' objectives as measured by the respective criteria, simultaneously meet the dual objectives with results intuitively comprehensible. Its low computing costs and parameter adjustment flexibility make the proposed framework a convenient approach for real-world multi-objective regionalization tasks. We conclude the research with discussions on the boundary conditions for the framework to work and their relevance to city science theories, along with practical implications. [ABSTRACT FROM AUTHOR]

The sociology of elites has long considered families as the unit of analysis in studies of power dynamics between elite dynasties and their transmission of wealth and prestige over generations. However, the assumption that families are cohesive units with common goals and agendas does not hold, especially for large and powerful family dynasties. Internal conflicts and clan rivalries throughout history suggest that independent clans, rather than families, are the more appropriate level for aggregation. The increasing availability of large-scale genealogical datasets and advances in social network analysis allow this more fine-grained perspective to be implemented even without historical documentation on observed clan structures. This paper builds on socio-anthropological conceptualizations of kinship and on hierarchical clustering techniques to present a new method for identifying independent clans within families that relies only on network-dependent terms. I use simulated data and an empirical kinship network of families of early modern Basel, Switzerland to compare a clan detector algorithm's performance with common community detection techniques. The historical accuracy of the clan structures detected is further assessed with various status indicators. The analyses show that the proposed clan detector algorithm is more suitable for identifying historically accurate clans than the traditional approaches. The application of the new method to the kinship network of Basel families sheds light on the city's stratification into high- and low-status societies in which elite families were also divided into privileged and less privileged clans. • New method for identifying independent clans within large family dynasties. • The algorithm's performance is assessed with simulated and empirical data. • Comparison with other community detection techniques. • The detected clans in the empirical network are historically accurate. [ABSTRACT FROM AUTHOR]

For different currents in policy analysis as policy networks and the Advocacy Coalition Framework (ACF), identifying coalitions from policy beliefs and coordination between actors is crucial to a precise understanding of a policy process. Focusing particularly the relational dimension of ACF approaches linked with policy network analysis, determining policy subsystems from the actor collaborations and exchanges has recently begun offering fertile links with the network analysis. Studies in this way frequently apply Block Modeling and Community Detection (BMCD) strategies to define homogeneous political groups. However, the BMCD literature is growing quickly, using a wide variety of algorithms and interesting selection methods that are much more diverse than those used in the policy network analysis and particularly the ACF when this current focused on the collaboration networks before or after regarding the belief distance between actors. Identifying the best methodological option in a specific context can therefore be difficult and few ACF studies give an explicit justification. On the other hand, few BMCD publications offer a systematic comparison of real social networks and they are never applied to policy network datasets. This paper offers a new, relevant 5-Step selection method to reconcile advances in both the policy networks/ACF and BMCD. Using an application based on original African policy network data collected in Madagascar and Niger, we provide a useful set of practical recommendations for future ACF studies using policy network analysis: (i) the density and size of the policy network affect the identification process, (ii) the "best algorithm" can be rigorously determined by maximizing a novel indicator based on convergence and homogeneity between algorithm results, (iii) researchers need to be careful with missing data: they affect the results and imputation does not solve the problem. • Identifying policy coalitions is fundamental to understand a policy network. • Studies apply different strategies but identifying the best option can be difficult. • We offer a relevant and consistent 5-Step method by maximizing a novel indicator. • Further research need to be careful with missing data. [ABSTRACT FROM AUTHOR]

In this article, we present various algorithms to partition the nodes of a network into groups that show the property of nestedness. Since perfect nestedness is a rare phenomenon, we consider the task from a data mining perspective, and we search for groups having high-level of nestedness. We utilize both agglomerative and divisive hierarchical clustering procedures and compare them on several benchmark and real-life networks. Furthermore, we propose different metrics derived from the results of our algorithms. We show that average-linkage and complete-linkage clustering can recover the largest fully nested clusters, and that the cluster size-weighted mean nestedness was a more stable metric for measuring clustering performance. Our proposed algorithms allow us to create multiple resolution views of nestedness-based clustering of networks, extending the field of graph-based data mining. [ABSTRACT FROM AUTHOR]

Many hierarchical methods for community detection in multicolored networks are capable of finding clusters when there are interslice correlation between layers. However, in general, they aggregate all the links in different layer treating them as being equivalent. Therefore, such aggregation might ignore the information about the relevance of a dimension in which the node is involved. In this paper, we fill this gap by proposing a hierarchical classification-based Louvain method for interslice-multicolored networks. In particular, we define a new node centrality measure named Attractivity to describe the inter-slice correlation that incorporates within and across-dimension topological features in order to identify the relevant dimension. Then, after merging dimensions through a frequential aggregation, we group nodes by their relational and attribute similarity, where attributes correspond to their relevant dimensions. We conduct an extensive experimentation using seven real-world multicolored networks, which also includes comparison with state-of-the-art methods. Results show the significance of our proposed method in discovering relevant communities over multiple dimensions and highlight its ability in producing optimal covers with higher values of the multidimensional version of the modularity function. [ABSTRACT FROM AUTHOR]

Community detection in complex networks is an important task for discovering hidden information in network analysis. Neighborhood density between nodes is one of the fundamental indicators of community presence in the network. A community with a high edge density will have correlations between nodes that extend beyond their immediate neighbors, denoted by motifs. Motifs are repetitive patterns of edges observed with high frequency in the network. We proposed the PCDMS method (Probabilistic Community Detection with Motif Structure) that detects communities by estimating the triangular motif in the network. This study employs structural density between nodes, a key concept in graph analysis. The proposed model has the advantage of using a probabilistic generative model that calculates the latent parameters of the probabilistic model and determines the community based on the likelihood of triangular motifs. The relationship between observing two pairs of nodes in multiple communities leads to an increasing likelihood estimation of the existence of a motif structure between them. The output of the proposed model is the intensity of each node in the communities. The efficiency and validity of the proposed method are evaluated through experimental work on both synthetic and real-world networks; the findings will show that the community identified by the proposed method is more accurate and dense than other algorithms with modularity, NMI, and F1score evaluation metrics. [ABSTRACT FROM AUTHOR]

Link prediction is an important task in social network analysis. Considering that the properties of nodes in social networks are generally inaccurate, it is more reliable and effective to use the network structure features to predict the links in the network. However, a central challenge of such methods is how to fully mine and utilize the network structure information. Here, we introduce a deep structure link prediction model (DSLP), whose idea is to integrate multiple types of community structures and multiple topology features into one probability model. We detect three types of community structures, disjoint, crisp overlap and fuzzy overlap, and then design an edge probability parameter to reflect their importance. Additionally, we propose an effective method to aggregate multiple topology features based on nodes and paths. We perform extensive experiments on artificial networks and real-world social networks to compare the proposed method with nine baseline algorithms, and the results show that our method offers higher precision than that of these well-known approaches. Finally, we discuss the method of integrating trusted node properties and feature selection. [ABSTRACT FROM AUTHOR]

Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of "events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as "archetypes" characterized by a unique combination of quantitative dimensions that we call "facets". Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality. [ABSTRACT FROM AUTHOR]

Semantic code clone detection is to find code snippets that are structurally or syntactically different, but semantically identical. It plays an important role in software reuse, code compression. Many existing studies have achieved good performance in non-semantic clone, but semantic clone is still a challenging task. Recently, several works have used tree or graph, such as Abstract Syntax Tree (AST), Control Flow Graph (CFG) or Program Dependency Graph (PDG) to extract semantic information from source codes. In order to reduce the complexity of tree and graph, some studies transform them into node sequences. However, this transformation will lose some semantic information. To address this issue, we propose a novel high-performance method that utilizes community detection to extract features of AST while preserving its semantic information. First, based on the AST of source code, we exploit community detection to split AST into different subtrees to extract the underlying semantics information of different code blocks, and use centrality analysis to quantify the semantic information as the weight of AST nodes. Then, the AST is converted into a sequence of tokens with weights, and a Siamese neural network model is used to detect the similarity of token sequences for semantic code clone detection. Finally, to evaluate our approach, we conduct experiments on two standard benchmark datasets, Google Code Jam (GCJ) and BigCloneBench (BCB). Experimental results show that our model outperforms the eight publicly available state-of-the-art methods in detecting code clones. It is five times faster than the tree-based method (ASTNN) in terms of time complexity. [ABSTRACT FROM AUTHOR]

With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques. [ABSTRACT FROM AUTHOR]

AbstractHigher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this article we propose the sub-Gaussian) tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound under sub-Gaussian noise, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the l2,∞ tensor perturbation bound for HOOI under independent, heteroscedastic, sub-Gaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

Background: Personal recovery is valued by people with bipolar disorder (BD), yet its conceptualisation is unclear. Prior work conceptualising personal recovery has focussed on qualitative evidence or clinical factors without considering broader psychosocial factors. This study used a network analysis of Bipolar Recovery Questionnaire (BRQ) responses, aiming to identify (1) independent relationships between items to identify those most "central" to personal recovery and (2) how the relationships between items reflect themes of personal recovery. Methods: The model was developed from BRQ responses (36 items) from 394 people diagnosed with bipolar disorder. The undirected network was based on a partial correlation matrix and was weighted. Strength scores were calculated for each node. Community detection analysis identified potential themes. The accuracy of the network was assessed using bootstrapping. Results: Two consistent communities were identified: "Access to meaningful activity" and "Learning from experiences." "I feel confident enough to get involved in things in life that interest me" was the strongest item, although the strength stability coefficient (0.36) suggested strength should be interpreted with caution. The average edge weight was 0.02; however, stronger edges were identified. Limitations: The network showed low stability, possibly due to sample heterogeneity. Future work could incorporate demographic variables, such as time since BD diagnosis or stage of personal recovery, into network estimation. Conclusions: Network analysis can be applied to personal recovery, not only clinical symptoms of BD. Clinical applications could include tailoring recovery‐focussed therapies towards encouraging important aspects of recovery, such as feeling confident to get involved with life. [ABSTRACT FROM AUTHOR]

Estimating the number of latent factors underlying a set of dichotomous items is a major challenge in social and behavioral research. Mokken scale analysis (MSA) and exploratory graph analysis (EGA) are approaches for partitioning measures consisting of dichotomous items. In this study we perform simulation-based comparisons of two EGA methods (EGA with graphical least absolute shrinkage and selector operator; EGAtmfg with triangulated maximally filtered graph algorithm), two MSA methods (AISP: automated item selection procedure; GA: genetic algorithm), and two widely used factor analytic techniques (parallel analysis with principal component analysis (PApc) and parallel analysis with principal axis factoring (PApaf)) for partitioning dichotomous items. Performance of the six methods differed significantly according to the data structure. AISP and PApc had highest accuracy and lowest bias for unidimensional structures. Moreover, AISP demonstrated the lowest rate of misclassification of items. Regarding multidimensional structures, EGA with GLASSO estimation and PApaf yielded highest accuracy and lowest bias, followed by EGAtmfg. In addition, both EGA techniques exhibited the lowest rate of misclassification of items to factors. In summary, EGA and EGAtmfg showed comparable performance to the highly accurate traditional method, parallel analysis. These findings offer guidance on selecting methods for dimensionality analysis with dichotomous indicators to optimize accuracy in factor identification. [ABSTRACT FROM AUTHOR]

Deep learning struggles with unsupervised tasks like community detection in networks. This work proposes the Enhanced Community Detection with Structural Information VGAE (VGAE-ECF) method, a method that enhances variational graph autoencoders (VGAEs) for community detection in large networks. It incorporates community structure information and edge weights alongside traditional network data. This combined input leads to improved latent representations for community identification via K-means clustering. We perform experiments and show that our method works better than previous approaches of community-aware VGAEs. [ABSTRACT FROM AUTHOR]

One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is a widely used model, and various estimation methods have been developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce a pairwise covariates-adjusted stochastic block model (PCABM), a generalization of SBM that incorporates pairwise covariate information. We study the maximum likelihood estimators of the coefficients for the covariates as well as the community assignments, and show they are consistent under suitable sparsity conditions. Spectral clustering with adjustment (SCWA) is introduced to efficiently solve PCABM. Under certain conditions, we derive the error bound of community detection for SCWA and show that it is community detection consistent. In addition, we investigate model selection in terms of the number of communities and feature selection for the pairwise covariates, and propose two corresponding algorithms. PCABM compares favorably with the SBM or degree-corrected stochastic block model (DCBM) under a wide range of simulated and real networks when covariate information is accessible. for this article are available online. [ABSTRACT FROM AUTHOR]

In many real-world networks, it is often observed that subgraphs or higher-order structures of certain configurations, e.g. triangles and by-fans, are overly abundant compared to standard randomly generated networks (Milo et al. 2002). However, statistical models accounting for this phenomenon are limited, especially when community structure is of interest. This limitation is coupled with a lack of community detection methods that leverage subgraphs or higher-order structures. In this paper, we propose a new community detection method that effectively uses higher-order structures in a network. Furthermore, for the community detection accuracy, under an edge-dependent network model that consists of both community and triangle structures, we develop a finite-sample error bound characterized by the expected triangle degree, which leads to the consistency of the proposed method. To the best of our knowledge, this is the first statistical error bound and consistency result for community detection of a single network considering a network model with dependent edges. We also show, in both simulation studies and a real-world data example, that our method unveils network communities that are otherwise invisible to methods that ignore higher-order structures. [ABSTRACT FROM AUTHOR]

Community detection is a crucial aspect in network analysis. Real-world networks are often enriched with attributes providing extensive information for nodes beyond mere topology. Integrating these nodal attributes into community detection for attributed networks poses notable challenges and remains an active research field. In this paper, we propose a novel method that incorporates structural information into fused attributes. This is achieved by defining a fusion similarity between nodes, which is a convex combination of topology similarity, pairwise attribute similarity, and attribute similarity with their immediate neighbors. One advantage of the proposed method is its flexibility in identifying communities in disassortative networks, where nodes exhibit more connections between different groups than within their own group. We employ an iterative spectral clustering technique to discover communities and assess the influence of various attributes within these communities. Our experimental results validate the effectiveness of this approach, demonstrating its utility in leveraging node attributes in diverse simulated and real-world network datasets. [ABSTRACT FROM AUTHOR]

Community detection is a significant research area in social networks. Most methods use network topology, but combining it with user interactions improves accuracy. This paper proposes a robust method to identify communities based on the improved user interaction degree, the weighted quasi-local structural similarity measure, and the frequent pattern mining on user interactions. In the community creation phase, influential users are identified based on eigenvector centrality and users who interact with them the most are extracted based on frequent pattern mining. In the community expansion phase, we introduce a measure to calculate the degree of user interactions based on the local clustering coefficient improved by interactions between common neighbors. We present two strategies to expand the community. The first strategy, a direct connection, exists between a user outside and a user inside the community. Their similarity is calculated based on the combined measure of improved user interaction degree and user degrees. The second strategy is if two users do not have a direct connection, we consider their communication paths. Therefore, we present a similarity measure combining a quasi-local path-based measure and an improved user interaction degree. Analysis of Higgs Twitter and Flickr datasets using internal density, Normalized Mutual Information, and Adjusted Rand Index shows that this paper's method outperforms the other five community detection methods. Furthermore, our method has more robustness than other relevant methods. [ABSTRACT FROM AUTHOR]

One of the integral part of the network analysis is finding groups of nodes that exhibit similar properties. Community detection techniques are a popular choice to find such groups or communities within a network and it relies on graph-based methods to achieve this goal. Finding communities in biological networks such as gene co-expression networks are particularly important to find groups of genes where we can focus on further downstream analysis and find valuable insights regarding concerned diseases. Here, we present an effective community detection method called community detection using centrality-based approach (CDCA), designed using the graph centrality approach. The method has been tested using four benchmark bulk RNA-seq datasets for schizophrenia and bipolar disorder, and the performance has been proved superior in comparison to several other counterparts. The quality of communities are determined using intrinsic graph properties such as modularity and homogeneity. The biological significance of resultant communities is decided using the pathway enrichment analysis. [ABSTRACT FROM AUTHOR]

Research in the area of identifying the most influential users in social networks is currently regarded to be one of the most important areas of study. Through the examination of the most influential users on social networks, it is possible to analyze and, in some cases, manage the dissemination of information. A technique that is both quick and scalable is proposed in this research as a means of identifying the users with maximum diffusion capabilities in online social networks. This approach is suited for directed networks as well as undirected networks. The approach that has been suggested is comprised of four stages: (1) community detection, which involves the partial partitioning of the whole social network into communities that are connected to one another by the use of the Louvain algorithm; (2) the removal of communities that are not suitable; (3) selection of prominent nodes within the particular community; and (4) selection of the top k seed nodes. Experimental research was carried out on a number of datasets, each of which was of a different complexity. Using imperfect social networks, it has been demonstrated that the findings generate better outcomes for the diffusion of influence than the current related work models, and they do so with a much less amount of processing time being required. [ABSTRACT FROM AUTHOR]

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Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdős–Rényi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdős–Rényi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Given only one snapshot of the final network G ⁠ , we study the problem of constructing confidence sets for the root node of the unobserved growth process; the root node can be patient zero in an infection network or the source of fake news in a social network. We propose inference algorithms based on Gibbs sampling that scales to networks with millions of nodes and provide theoretical analysis showing that the size of the confidence set is small if the noise level of the ER edges is not too large. We also propose variations of the model in which multiple growth processes occur simultaneously, reflecting the growth of multiple communities; we use these models to provide a new approach to community detection. [ABSTRACT FROM AUTHOR]

Despite great progress in enhancing the efficiency of public transport, one still cannot seamlessly incorporate structural characteristics into existing algorithms. Moreover, comprehensively exploring the structure of urban bus networks through a single-view modelling approach is limited. In this research, a multi-view graph learning algorithm (MvGL) is proposed to aggregate community information from multiple views of urban bus system. First, by developing a single-view graph encoder module, latent community relationships can be captured during learning node embeddings. Second, inspired by attention mechanism, a multi-view graph encoder module is designed to fuse node embeddings in different views, aims to perceive more community information of urban bus network comprehensively. Then, the community assignment can be updated by using a differentiable clustering layer. Finally, a well-defined objective function, which integrates node level, community level and graph level, can help improve the quality of community detection. Experimental results demonstrated that MvGL can effectively aggregate community information from different views and further improve the quality of community detection. This research contributes to the understanding the structural characteristics of public transport networks and facilitates their operational efficiency. [ABSTRACT FROM AUTHOR]

A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes. [ABSTRACT FROM AUTHOR]

Abstract The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

The rise of data from smart city services and the emergence of advanced algorithms have emphasized the need for a deeper understanding of the underlying patterns of urban mobility and the potential opportunities for more efficient urban planning and policymaking. By identifying communities with similar mobility patterns, researchers can gain better insights into how people move within and between different regions. Traditional community detection methodologies mainly focused on identifying geographic communities defined by shared locations. However, this perspective overlooks the broader definition of communities. Communities can also emerge from shared social interests, collective actions, and activity patterns. This implies that geographically disparate areas might exhibit similar patterns, which is particularly relevant in the study of mobility pattern similarity. Rather than focusing on regions with strong spatial interactions, this study aims to identify regions that show more similarity to each other than to other areas. Such similarities may indicate parallel urban functionalities, which are essential for effective urban planning and policymaking. To bridge this gap, our study introduces a customized community detection algorithm that employs Dynamic Time Warping (DTW) to quantitatively assess the similarity in mobility patterns between different communities. This advanced approach not only improves the identification of comparable mobility patterns but also demonstrates remarkable flexibility, broadening its application to various other social phenomena. The results demonstrate the effectiveness of the proposed model in capturing complex mobility patterns across different locations and days of the week.

Abstract With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques.

Dynamic (Temporal) graphs capture the valuable evolution of real-world systems, from the continuously evolving patterns of social interactions and genetic pathways to the dynamic fluctuations of economic forces. Detecting communities for such evolving networks poses unique challenges. Detecting and analyzing the evolution of communities within dynamic graphs unlocks valuable insights into the underlying structural and temporal patterns of real-world systems. However, the sheer volume of modern graph data and the inherent complexity of the temporal dimension pose significant challenges to scalable community detection algorithms. Addressing this gap, our work explores the limited landscape of scalable distributed-memory parallel methods specifically designed for dynamic network community detection. We propose a novel parallel algorithm, DyG-DPCD (Dynamic Graph Distributed Parallel Community Detection), to detect communities in dynamic networks using the Message Passing Interface (MPI) framework. We present a vertex-centric approach, allowing us to detect communities through local optimization. Furthermore, we enhance our baseline algorithm by incorporating three heuristics, which improve the algorithm’s performance significantly while maintaining the quality of the solutions. We demonstrate the efficiency of our algorithm by experimenting on several real-world large-scale networks with hundreds of millions of edges spanning diverse domains. Notably, DyG-DPCD achieves speedups between 25 × and 30 × for large networks that we experimented on using NERSC compute nodes. Our algorithm outperforms the STINGER parallel re-agglomeration algorithm by 30 × . [ABSTRACT FROM AUTHOR]

Network-based Semi-Supervised Clustering (NeSSC) is a semi-supervised approach for clustering in the presence of an outcome variable. It uses a classification or regression model on resampled versions of the original data to produce a proximity matrix that indicates the magnitude of the similarity between pairs of observations measured with respect to the outcome. This matrix is transformed into a complex network on which a community detection algorithm is applied to search for underlying community structures which is a partition of the instances into highly homogeneous clusters to be evaluated in terms of the outcome. In this paper, we focus on the case the outcome variable to be used in NeSSC is numeric and propose an alternative selection criterion of the optimal partition based on a measure of overlapping between density curves as well as a penalization criterion which takes accounts for the number of clusters in a candidate partition. Next, we consider the performance of the proposed method for some artificial datasets and for 20 different real datasets and compare NeSSC with the other three popular methods of semi-supervised clustering with a numeric outcome. Results show that NeSSC with the overlapping criterion works particularly well when a reduced number of clusters are scattered localized. [ABSTRACT FROM AUTHOR]

The urban space community is the basic unit used to demonstrate the urban spatial structure, and the space community structure of island-oriented cities is different from that of general inland cities because they are formed based on the island and the inland, respectively. Through the case study of Xiamen, based on smart card data, this paper divides the obtained origin–destination data into local and global levels with trip cutoff thresholds. The distribution of city centers is analyzed by detecting the overall urban space community structure and the two-level intraisland and cross-island urban space community structure with the PageRank and community detection algorithms. The results revealed that: (1) in the spatial structure of the island-oriented city, the cores are clustered on the island, while the centers are distributed in bands and dots outside the island; (2) the community units are distributed across the island; and (3) the space community boundary of the island-oriented city is quite different from the artificially defined administrative unit boundary. To summarize, by enhancing the delineation of urban spatial communities using the Gaussian kernel function and least-squares cross-validation, improving the model granularity, and integrating data from multiple public transport modes, this paper achieved a more precise division of urban spatial community structures. Consequently, it revealed the distinctive urban form and developmental pattern of the island-oriented city, holding significant implications for investigating urban spatial layout studies. [ABSTRACT FROM AUTHOR]

Existing methods for detecting communities in attributed social networks often rely solely on network topology, which leads to suboptimal accuracy in community detection, inefficient use of available data, and increased time required for identifying groups. This paper introduces the Dual Embedding-based Graph Convolution Network (DEGCN) to address these challenges. This new method uses graph embedding techniques in a new deep learning framework to improve accuracy and speed up community detection by combining the nodes' content with the network's topology. Initially, we compute the modularity and Markov matrices of the input graph. Each matrix is then processed through a graph embedding network with at least two layers to produce a condensed graph representation. As a result, a multilayer perceptron neural network classifies each node's community based on these generated embeddings. We tested the suggested method on three standard datasets: Cora, CiteSeer, and PubMed. Then, we compared the outcomes to many basic and advanced approaches using five important metrics: F1-score, adjusted rand index (ARI), normalized mutual information (NMI), and accuracy. The findings demonstrate that the DEGCN accurately captures community structure, achieves superior precision, and has higher ARI, NMI, and F1 scores, significantly outperforming existing algorithms for identifying community structures in medium-scale networks. [ABSTRACT FROM AUTHOR]