Detecting Community Structure Research Articles

Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods.

Determining the best algorithm to detect community structures in networks: application to power systems

Determining the best algorithm to detect community structures in networks: application to power systems

Detecting community structures using Modified Fast Louvain Method in complex networks

Detecting community structures using Modified Fast Louvain Method in complex networks

Diffusion profile embedding as a basis for graph vertex similarity

AbstractWe describe here a notion ofdiffusion similarity, a method for defining similarity between vertices in a given graph using the properties of random walks on the graph to model the relationships between vertices. Using the approach of graph vertex embedding, we characterize a vertexviby considering two types of diffusion patterns: the ways in which random walks emanate from the vertexvito the remaining graph and how they converge to the vertexvifrom the graph. We define the similarity of two verticesviandvjas the average of the cosine similarity of the vectors characterizingviandvj. We obtain these vectors by modifying the solution to a differential equation describing a type of continuous time random walk.This method can be applied to any dataset that can be assigned a graph structure that is weighted or unweighted, directed or undirected. It can be used to represent similarity of vertices within community structures of a network while at the same time representing similarity of vertices within layered substructures (e.g., bipartite subgraphs) of the network. To validate the performance of our method, we apply it to synthetic data as well as the neural connectome of theC. elegansworm and a connectome of neurons in the mouse retina. A tool developed to characterize the accuracy of the similarity values in detecting community structures,the uncertainty index, is introduced in this paper as a measure of the quality of similarity methods.

The Influence of NMI against Modularity in Community Detection Problem: A Case Study for Unsigned and Signed Networks

Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algorithms have been implemented to unfold the structures of communities, the influence of NMI on the Q, and vice versa, between a detected partition and the correct partition in signed and unsigned networks is unclear. For this reason, in this paper, we investigate the correlation between Q and NMI in signed and unsigned networks. The results show that there is no direct relationship between Q and NMI in both types of networks.

Influence of number of individuals and observations per individual on a model of community structure.

Social network analysis is increasingly applied to understand animal groups. However, it is rarely feasible to observe every interaction among all individuals in natural populations. Studies have assessed how missing information affects estimates of individual network positions, but less attention has been paid to metrics that characterize overall network structure such as modularity, clustering coefficient, and density. In cases such as groups displaying fission-fusion dynamics, where subgroups break apart and rejoin in changing conformations, missing information may affect estimates of global network structure differently than in groups with distinctly separated communities due to the influence single individuals can have on the connectivity of the network. Using a bat maternity group showing fission-fusion dynamics, we quantify the effect of missing data on global network measures including community detection. In our system, estimating the number of communities was less reliable than detecting community structure. Further, reliably assorting individual bats into communities required fewer individuals and fewer observations per individual than to estimate the number of communities. Specifically, our metrics of global network structure (i.e., graph density, clustering coefficient, Rcom) approached the ‘real’ values with increasing numbers of observations per individual and, as the number of individuals included increased, the variance in these estimates decreased. Similar to previous studies, we recommend that more observations per individual should be prioritized over including more individuals when resources are limited. We recommend caution when making conclusions about animal social networks when a substantial number of individuals or observations are missing, and when possible, suggest subsampling large datasets to observe how estimates are influenced by sampling intensity. Our study serves as an example of the reliability, or lack thereof, of global network measures with missing information, but further work is needed to determine how estimates will vary with different data collection methods, network structures, and sampling periods.

Optimizing modularity with nonnegative matrix factorization

Community structure detection is one of the fundamental problems in complex network analysis towards understanding the topology structure and function of the network. Modularity is a criterion to evaluate the quality of community structures, and optimization of this quality function over the possible divisions of a network is a sensitive detection method for community structure. However, the direct application of this method is computationally costly. Nonnegative matrix factorization (NMF) is a widely used method for community detection. In this paper, we show that modularity maximization can be approximately reformulated under the framework of NMF with Frobenius norm, especially when [Formula: see text] is large. A new algorithm for detecting community structure is proposed based on the above finding. The new method is compared with four state-of-the-art methods on both synthetic and real-world networks, showing its higher clustering quality over the existing methods.

An Ensemble of Community Detection in Social Networks Using Clustering of Users Demographic and Topological Information

Background: One of the great challenges in social network analysis is community detection. Community is a group of users which have high intra connections and sparse inter connections. Community detection or clustering reveals community structure of social networks and hidden relationships among their constituents. Nowadays, many different methods are proposed to detect community structures in social networks from different perspectives, but none of them can be a constant winner. Therefore, an ensemble of different methods can potentially improve the final result. Methods: In this paper, we present a framework for different methods to be combined for community detection. This method is a combination of genetic algorithms, particle swarm optimization, kmeans clustering and Louvain clustering algorithms. Our method uses topological and demographic information to identify communities and can automatically determine the number of optimal communities. Results and Conclusion: Quantitative evaluations based on extensive experiments on Ego- Facebook social network dataset reveals that the method presented in this study achieves favorable results, which are quite superior to other relevant algorithms in the literature. • Discovering relationships between individuals by analyzing social networks. • Providing identifying communities algorithms based on different clustering methods. • An ensemble of community detection consisting of GA, PSO, k-means and Louvain clustering. • The proposed method is better than the TSA method at silhouette and modularity criterion. Demographic information also relates to the profile of users and their shared tweets.

A Community Structure Enhancement-Based Community Detection Algorithm for Complex Networks

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.

Community Detection in Large-Scale Bipartite Biological Networks.

Networks are useful tools to represent and analyze interactions on a large, or genome-wide scale and have therefore been widely used in biology. Many biological networks—such as those that represent regulatory interactions, drug-gene, or gene-disease associations—are of a bipartite nature, meaning they consist of two different types of nodes, with connections only forming between the different node sets. Analysis of such networks requires methodologies that are specifically designed to handle their bipartite nature. Community structure detection is a method used to identify clusters of nodes in a network. This approach is especially helpful in large-scale biological network analysis, as it can find structure in networks that often resemble a “hairball” of interactions in visualizations. Often, the communities identified in biological networks are enriched for specific biological processes and thus allow one to assign drugs, regulatory molecules, or diseases to such processes. In addition, comparison of community structures between different biological conditions can help to identify how network rewiring may lead to tissue development or disease, for example. In this mini review, we give a theoretical basis of different methods that can be applied to detect communities in bipartite biological networks. We introduce and discuss different scores that can be used to assess the quality of these community structures. We then apply a wide range of methods to a drug-gene interaction network to highlight the strengths and weaknesses of these methods in their application to large-scale, bipartite biological networks.

Community Detection Research Based on Line Graph

Community structure is an important property of complex networks, in which network nodes are tightly split into different groups. In this paper, a new overlapping community detection algorithm based on line graph is proposed. Spectral analysis based on Normal Laplace Matrix of line graph is introduced. We not only make the spectral clustering to be an overlapping algorithm but also define a new similarity between edges by spectral analysis. We test our method on three-community network and Zachary karate club network. The experimental result shows that our method can detect community structure and overlapping nodes effectively. It is worth to note that our method is against undirected and unweighted graphs.

Detecting community structure in wild populations: a simulation study based on male elephant, Loxodonta africana, data

Many social networks exhibit community structure, where individuals form discrete subgroups. The composition of such groupings is important for numerous research directions, but their characterization is challenged by data sampling issues. In wild populations, where individuals range over large distances and observation can be limited, social data required to resolve community structure are difficult to collect. Recent studies used simulated data sets to determine the robustness of individual level network metrics under suboptimal sampling conditions, but the sensitivity of community detection algorithms to imperfect sampling has not been assessed. Here, we used simulated data sets to determine how sampling effort and skew influence the ability of three community detection algorithms (fastgreedy, walktrap and louvain) to recover the ‘true’ community structure of networks under two sampling regimes (field-based observational sampling and sampling through biologgers, e.g. proximity detectors). We also examined the robustness of a measure of uncertainty in estimated community structure ( r com ). We based our simulated societies on contact patterns in wild male African elephants, a model system reflecting common sampling challenges of large wild populations. Our results indicate that the accuracy of the algorithms improved with increasing sampling effort and decreasing sampling skew. Under the field sampling regime, when sampling effort is constrained, mid-levels of sampling skew may provide a reasonable compromise between maximizing the mean numbers of observation per individual and minimizing sampling skew. Even with skewed data, r com can provide a reliable measure of uncertainty in the estimated community assignments, but it should be interpreted cautiously with highly skewed data. The network structures explored represent common sampling challenges for wild populations, but unexplored sampling regimes may drive somewhat different dynamics. Our simulations indicate that adequate sampling even when skewed can be informative and maximization of the number of observations among all individuals should be a general objective. • Useful information about community structure can be obtained with skewed sampling. • Accurate community assignment requires many observations of each individual. • Moderately skewed sampling can improve results while reducing sampling requirements. • r com is a robust measure of uncertainty even when sampling is skewed. • Results from biologgers are robust with as few as 30% of the population tagged.

Semi-supervised community detection on attributed networks using non-negative matrix tri-factorization with node popularity

The World Wide Web generates more and more data with links and node contents, which are always modeled as attributed networks. The identification of network communities plays an important role for people to understand and utilize the semantic functions of the data. A few methods based on non-negative matrix factorization (NMF) have been proposed to detect community structure with semantic information in attributed networks. However, previous methods have not modeled some key factors (which affect the link generating process together), including prior information, the heterogeneity of node degree, as well as the interactions among communities. The three factors have been demonstrated to primarily affect the results. In this paper, we propose a semi-supervised community detection method on attributed networks by simultaneously considering these three factors. First, a semi-supervised non-negative matrix tri-factorization model with node popularity (i.e., PSSNMTF) is designed to detect communities on the topology of the network. And then node contents are integrated into the PSSNMTF model to find the semantic communities more accurately, namely PSSNMTFC. Parameters of the PSSNMTFC model is estimated by using the gradient descent method. Experiments on some real and artificial networks illustrate that our new method is superior over some related state-of-the-art methods in terms of accuracy.

Influence maximization in social graphs based on community structure and node coverage gain

Influence maximization is an optimization problem in the area of social graph analysis, which asks to choose a subset of k individuals to maximize the number of influenced nodes at the end of the diffusion process. As individuals within a community have frequent contact and are more likely to influence each other, community-based influence maximization has attracted considerable attentions. However, this kind of works ignores the role of overlapping nodes in community structure, resulting in performance degradation in seeds selection. In addition, many existing community-based algorithms identify the final seeds only from the selected important communities, or they need to leverage the weights between local spread and global spread of a node. It is difficult to set suitable scales for important communities or to determine the weights for different spread. In this paper, we propose a novel influence maximization approach based on overlapping community structure and node coverage gain. Firstly, social graphs are partitioned into different overlapping communities by the algorithm of node location analysis in topological potential field. Secondly, a node coverage gain sensitive centrality measure is put up to evaluate the influence of each node locally within its belonging communities, which avoids the problem of local spread and global spread. Finally, seed nodes are directly selected by combining the detected community structure with the pre-designed strategy, without important communities identification. The comprehensive experiments under both the Uniform Independent Cascade model and the Weighted Independent Cascade model demonstrate that our proposed approach can achieve competitive influence spread, outperforming state-of-the-art works. Furthermore, our proposed approach exhibits stable performance on graphs with different scales and various structural characteristics.

A spiderweb model for community detection in dynamic networks

Community detection in dynamic networks is one of the most challenging tasks in the field of network analysis. In general, networks often evolve smoothly between successive snapshots. Therefore, the community structure detected in each snapshot should not only be of high quality but also reflect the smoothness of the variations compared with the previous snapshot. In this paper, we propose a novel incremental community-detection method named Spiderweb, which detects the community structure in each snapshot by simulating the evolution of spiderwebs. We categorize the evolutionary events of the network into three types, and then address the changed nodes and edges according to three corresponding evolution rules. In this procedure, some nodes are assigned to proper communities. Then, we construct a new subgraph for the unclassified changed nodes, and detect its communities efficiently. Finally, we merge some communities to obtain the resulting community structure. We conduct extensive experiments on both artificial networks and real-world networks to test the proposed method, and the experimental results show the superiority of the proposed method over some state-of-the-art algorithms in terms of both the quality and the temporal smoothness of the detected community structures. The proposed method provides us with a stable and promising solution for the problem of community detection in dynamic networks.

A classification of community detection methods in social networks: a survey

ABSTRACT The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very large number of papers has already been published in the literature. Even more important is the fact that, this large number of articles is in fact spread across a large number of different disciplines, from computer science, to statistics, and social sciences. These facts necessitate some type of classification and organization of these works. In this work, our basic classification approach divides the community detection schemes into three basic approaches: (a) the bottom-up approaches, (b) the top-down approaches, and (c) the data structure-based approaches. The first category includes the majority of algorithms, so further classification is possible. Such a classification is included in this work. For the other two categories, we make no further categorizations but we simply focus our discussion on the metrics or the data structures being used. Finally, a few possible directions for future research are also suggested.

Detection of Community Structures in Dynamic Social Networks Based on Message Distribution and Structural/Attribute Similarities

Community detection is a crucial challenge in social network analysis. This task is important because it gives leads to study emerging phenomena. Indeed, it makes it possible to identify the different communities representing individuals with common interests and/or strong connections between them. In addition, it allows tracking the transformation of these communities over time. In this work, we propose a dynamic community detection approach called Attributes, Structure, and Messages distribution-based approach (ASMsg). In addition to the node attributes and the topological structure of the network, we use the rate of transferred messages as the key concept of this approach. Therefore, we obtain communities with similar members that are strongly connected and also frequently interacting. Furthermore, the proposed approach is able to detect all possible communities’ transformations even if the communities are overlapped. To demonstrate its efficiency, we widely test ASMsg on artificial and real-world dynamic networks and compare it with representative methods. The results show the superiority of our approach in terms of detected communities.

Robust Community Detection in Graphs

Community detection in network-type data provides a powerful tool in analyzing and understanding real-world systems. In fact, community detection approaches aim to reduce the network’s dimensionality and partition it into a set of disjoint clusters or communities. However, real networks are usually corrupted with noise or outliers which affect the detected community structure quality. In this paper, a new robust community detection algorithm that is capable of recovering a clean or a smoothed version of the corrupted graph and detecting the correct community structure is introduced. The proposed approach combines robust principal component analysis (RPCA) and symmetric nonnegative matrix factorization (SymNMF) in a single optimization problem. The proposed problem is solved under the framework of alternating direction methods of multipliers (ADMM). In particular, the corrupted adjacency matrix is decomposed into a low-rank and sparse components using RPCA and the community structure is detected by applying SymNMF to the extracted low-rank component. Extensive experiments that have been conducted on real and simulated binary and weighted networks show that the proposed approach significantly outperforms existing algorithms in detecting the correct community structure even in grossly corrupted networks.

Community structure detection algorithm based on link prediction

Community structure detection algorithm based on link prediction

Community structure detection algorithm based on link prediction

Community structure identification has received a great effort among computer scientists who are focusing on the properties of complex networks. The label propagation algorithm is a near linear tim...