Community Detection In Networks Research Articles

A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.

Leveraging cascading information for community detection in social networks

Information exchange among individuals is one of the key factors leading to formation of modular structures in social networks, often referred as communities. In this paper, we devise a novel information diffusion based approach to leverage the latent information exchange among the individuals for identifying communities. Designed an algorithm called PraSar, which has mainly two phases namely seeding and unification. In seeding phase, closely connected groups of nodes called seed communities are identified by analyzing the cascades obtained during information diffusion. In unification phase, the external connections of seed communities are reduced by unifying two or more seed communities. Involvement of cascade that are formed during the diffusion process enables PraSar to operate locally and giving an overall complexity of O(n2). The theoretical foundation of the proposed approach is established by various theorems. Empirical results on diverse real world datasets are evident for the effectiveness and competitiveness of the PraSar algorithm over state-of-the-art community detection algorithms.

Parkinsonism, Psychomotor Slowing, Negative and Depressive Symptoms in Schizophrenia Spectrum and Mood Disorders: Exploring Their Intricate Nexus Using a Network Analytic Approach.

Parkinsonism, psychomotor slowing, negative and depressive symptoms show evident phenomenological similarities across different mental disorders. However, the extent to which they interact with each other is currently unclear. Here, we hypothesized that parkinsonism is an independent motor abnormality showing limited associations with psychomotor slowing, negative and depressive symptoms in schizophrenia spectrum (SSD), and mood disorders (MOD). We applied network analysis and community detection methods to examine the interplay and centrality (expected influence [EI] and strength) between parkinsonism, psychomotor slowing, negative and depressive symptoms in 245 SSD and 99 MOD patients. Parkinsonism was assessed with the Simpson-Angus Scale (SAS). We used the Positive and Negative Syndrome Scale (PANSS) to examine psychomotor slowing (item #G7), negative symptoms (PANSS-N), and depressive symptoms (item #G6). In SSD and MOD, PANSS item #G7 and PANSS-N showed the largest EI and strength as measures of centrality. Parkinsonism had small or no influence on psychomotor slowing, negative and depressive symptoms in SSD and MOD. In SSD and MOD, exploratory graph analysis identified one community, but parkinsonism showed a small influence on its occurrence. Network Comparison Test yielded no significant differences between the SSD and MOD networks (global strength p value: .396 and omnibus tests p value: .574). The relationships between the individual domains followed a similar pattern in both SSD and MOD highlighting their transdiagnostic relevance. Despite evident phenomenological similarities, our results suggested that parkinsonism is more independent of negative and depressive symptoms than psychomotor slowing in both SSD and MOD.

Survey on graph neural network-based community detection and its applications

Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks.

The equivalences of community detection methods for bipartite networks

Abstract Investigating the community structures of bipartite networks is a frequent topic of discussion in the ecological and social fields. The most widely used methods, as proposed by numerous academics from varying perspectives, include spectral graph partitioning, modularity, nonnegative matrix factorization, and stochastic block model. In this paper, we demonstrate three equivalences among these four methods. One, both Dhillon spectral graph partitioning and Barber modularity clustering are equivalent to solving for the matrix's left and right singular vectors after relaxing the discrete constraints. Two, the nonnegative matrix factorization clustering is equivalent to the Dhillon spectral graph partitioning. Three, The bipartite stochastic block model is equivalent to the constraint-based NMF that uses K-L divergence as its cost function. These equivalences, obtained through rigorous mathematical derivations, will aid in the future development of efficient algorithms for community detection in bipartite networks.

Detecting Overlapping Communities Based on Influence-Spreading Matrix and Local Maxima of a Quality Function

Community detection is a widely studied topic in network structure analysis. We propose a community detection method based on the search for the local maxima of an objective function. This objective function reflects the quality of candidate communities in the network structure. The objective function can be constructed from a probability matrix that describes interactions in a network. Different models, such as network structure models and network flow models, can be used to build the probability matrix, and it acts as a link between network models and community detection models. In our influence-spreading model, the probability matrix is called an influence-spreading matrix, which describes the directed influence between all pairs of nodes in the network. By using the local maxima of an objective function, our method can standardise and help in comparing different definitions and approaches of community detection. Our proposed approach can detect overlapping and hierarchical communities and their building blocks within a network. To compare different structures in the network, we define a cohesion measure. The objective function can be expressed as a sum of these cohesion measures. We also discuss the probability of community formation to analyse a different aspect of group behaviour in a network. It is essential to recognise that this concept is separate from the notion of community cohesion, which emphasises the need for varying objective functions in different applications. Furthermore, we demonstrate that normalising objective functions by the size of detected communities can alter their rankings.

Motif-based community detection in heterogeneous multilayer networks

Multilayer networks composed of intralayer edges and interlayer edges are an important type of complex networks. Considering the heterogeneity of nodes and edges, it is necessary to design more reasonable and diverse community detection methods for multilayer networks. Existing research on community detection in multilayer networks mainly focuses on multiplexing networks (where the nodes are homogeneous and the edges are heterogeneous), but few studies have focused on heterogeneous multilayer networks where both nodes and edges represent different semantics. In this paper, we studied community detection on heterogeneous multilayer networks and proposed a motif-based detection algorithm. First, the communities and motifs of multilayer networks are defined, especially the interlayer motifs. Then, the modularity of multilayer networks based on these motifs is designed, and the community structure of the multilayer network is detected by maximizing the modularity of multilayer networks. Finally, we verify the effectiveness of the detection algorithm on synthetic networks. In the experiments on synthetic networks, comparing with the classical community detection algorithms (without considering interlayer heterogeneity), the motif-based modularity community detection algorithm can obtain better results under different evaluation indexes, and we found that there exists a certain relationship between motifs and communities. In addition, the proposed algorithm is applied in the empirical network, which shows its practicability in the real world. This study provides a solution for the investigation of heterogeneous information in multilayer networks.

Distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks

This paper proposes a distributed pseudo-likelihood method (DPL) to conveniently identify the community structure of large-scale networks. Specifically, we first propose a block-wise splitting method to divide large-scale network data into several subnetworks and distribute them among multiple workers. For simplicity, we assume the classical stochastic block model. Then, the DPL algorithm is iteratively implemented for the distributed optimization of the sum of the local pseudo-likelihood functions. At each iteration, the worker updates its local community labels and communicates with the master. The master then broadcasts the combined estimator to each worker for the new iterative steps. Based on the distributed system, DPL significantly reduces the computational complexity of the traditional pseudo-likelihood method using a single machine. Furthermore, to ensure statistical accuracy, we theoretically discuss the requirements of the worker sample size. Moreover, we extend the DPL method to estimate degree-corrected stochastic block models. The superior performance of the proposed distributed algorithm is demonstrated through extensive numerical studies and real data analysis.

Comparative Study of Kinship Network Community Detection Approaches

Comparative Study of Kinship Network Community Detection Approaches

Community Detection in Multiplex Networks Using Orthogonal Non-Negative Matrix Tri-Factorization Based on Graph Regularization and Diversity

In recent years, community detection has received increasing interest. In network analysis, community detection refers to the identification of tightly connected subsets of nodes, which are called “communities” or “groups”, in the network. Non-negative matrix factorization models are often used to solve the problem. Orthogonal non-negative matrix tri-factorization (ONMTF) exhibits significant potential as an approach for community detection within multiplex networks. This paper explores the application of ONMTF in multiplex networks, aiming to detect both shared and exclusive communities simultaneously. The model decomposes each layer within the multiplex network into two low-rank matrices. One matrix corresponds to shared communities across all layers, and the other to unique communities within each layer. Additionally, graph regularization and the diversity of private communities are taken into account in the algorithm. The Hilbert Schmidt Independence Criterion (HSIC) is used to constrain the independence of private communities. The results prove that ONMTF effectively addresses community detection in multiplex networks. It also offers strong interpretability and feature extraction capabilities. Therefore, it is an advanced method for community detection in multiplex networks.

Community Detection in Network: Algorithmic Approaches with Python Programming

Community Detection in Network: Algorithmic Approaches with Python Programming

CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks

CSAT: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks

Network community detection using higher-order structures

Summary 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.

RW-HeCo: A random walk and network centrality based graph neural network for community detection in heterogeneous networks

RW-HeCo: A random walk and network centrality based graph neural network for community detection in heterogeneous networks

Local Community Detection in Multiple Private Networks

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the Local Collaborative Community Detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network’s private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

Deciphering the interplay between psychopathological symptoms, sensorimotor, cognitive and global functioning: a transdiagnostic network analysis.

Understanding the relationship between psychopathology and major domains of human neurobehavioral functioning may identify new transdiagnostic treatment targets. However, studies examining the interrelationship between psychopathological symptoms, sensorimotor, cognitive, and global functioning in a transdiagnostic sample are lacking. We hypothesized a close relationship between sensorimotor and cognitive functioning in a transdiagnostic patient sample. We applied network analysis and community detection methods to examine the interplay and centrality [expected influence (EI) and strength] between psychopathological symptoms, sensorimotor, cognitive, and global functioning in a transdiagnostic sample consisting of 174 schizophrenia spectrum (SSD) and 38 mood disorder (MOD) patients. All patients (n = 212) were examined with the Positive and Negative Syndrome Scale (PANSS), the Heidelberg Neurological Soft Signs Scale (NSS), the Global Assessment of Functioning (GAF), and the Brief Cognitive Assessment Tool for Schizophrenia consisted of trail making test B (TMT-B), category fluency (CF) and digit symbol substitution test (DSST). NSS showed closer connections with TMT-B, CF, and DSST than with GAF and PANSS. DSST, PANSS general, and NSS motor coordination scores showed the highest EI. Sensory integration, DSST, and CF showed the highest strength. The close connection between sensorimotor and cognitive impairment as well as the high centrality of sensorimotor symptoms suggests that both domains share aspects of SSD and MOD pathophysiology. But, because the majority of the study population was diagnosed with SSD, the question as to whether sensorimotor symptoms are really a transdiagnostic therapeutic target needs to be examined in future studies including more balanced diagnostic groups.

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

A motif-based probabilistic approach for community detection in complex networks

AbstractCommunity 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.

Outlier Detection and Prediction in Evolving Communities

Community detection in social networks is of great importance and is used in a variety of applications such as recommendation systems and targeted advertising. While detecting dense groups with high levels of connectivity and similar interests between their members is the main target of traditional network analysis, finding network members with quite different behavior than the majority of nodes is important as well. These nodes are known as outliers, and their accurate detection can be very useful; when outliers are marked as noisy nodes, their early exclusion from analysis can lead to high computational profits. On the other hand, they can represent interesting components that call for further investigation to find the reasons for their outlying behavior and possible ways to include them in a neighboring community. Both community and outlier detection are challenging in temporal environments where changes occur in real time; thus, dynamic methods need to be deployed rather than to static methods. In our work, we take into account the content of the network, in contrast to most of related studies, where only the network’s structure contributes to community formation. We define an adaptive outlier score to be assigned to each node in order to quantify its outlierness, and introduce a complete online community detection algorithm that analyzes both the network’s structure and content while at the same time detecting community outliers. To evaluate our method, we retrieved and processed two real datasets regarding social networks with temporal and content information. Experimental results show that our method is capable of detecting outliers in real-time evolving communities and provides an outlier score which is a better metric of each node’s outlierness compared to widely used metrics. Finally, experimental results indicate that our method is suitable for predicting the status of future nodes based on their current outlier score.

U.S. immigration policy at the court: organized interests & asymmetric decisions

ABSTRACT Do some interest groups fare better on immigration cases before the Supreme Court? We build on interest group power theory and extend the literature by focusing on the heterogeneity of signers to amicus curiae briefs. Specifically, we argue that the presence of organizations that collaborate with multiple communities sends stronger signals to the justices. We use network community detection methods to reveal the coalition behavior of amici that file briefs on immigration cases between 1947 and 2021. We find that immigration cases garner far more amici on the liberal than conservative side, and that amici coalition behavior varies from acting alone, to cliques, to multiple communities. The presence of an amicus that works in multiple communities has a significant and asymmetric effect on judicial decision-making, such that conservative amici move justices to decide in their favor, but there is no comparable effect for liberal groups. Moreover, the effect is conditional on justice ideology, with moderate justices moving to side with conservative amici. In the domain of immigration cases, the accent heard by justices is that of the conservative side.