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Solving dynamic overlapping community detection problem by a multiobjective evolutionary algorithm based on decomposition

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 54卷

作者： Wan, Xing Zuo, Xingquan Song, Feng Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Minist Educ Key Lab Trustworthy Distributed Comp & Serv Beijing Peoples R China

Dynamic and overlapping are two common features of community structures for many real world complex networks. Although there are few studies on detecting dynamic overlapping communities, all those studies only consider a single optimization objective. In practice, it is necessary to evaluate the community detection by multiple metrics to reflect different aspects of a community structure and those metrics may conflict with each other. In this paper, we propose a multi-objective approach based on decomposition for the problem of dynamic overlapping community detection, with consideration of three optimization objectives: partition density (D), extended modularity (EQ), and improved mutual information (NMILFK). The dynamic overlapping network can be regarded as a set of network snapshots. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to detect the communities for each snapshot. To improve the search efficiency, the dynamic optimization technique and a dynamic resource allocation strategy are introduced into the approach. Experiments show that our approach can find uniformly distributed Pareto solutions for the problem and outperforms those comparative approaches.

关键词： Multi-objective evolutionary algorithm overlapping community detection Dynamic community detection Dynamic optimization

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Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

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INFORMATION SCIENCES 2022年 608卷 1464-1479页

作者： He, Chaobo Zheng, Yulong Cheng, Junwei Tang, Yong Chen, Guohua Liu, Hai South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China

community detection in attributed graph is of great application value and many related methods have been continually presented. However, existing methods for community detection in attributed graph still cannot well solve three key problems simultaneously: link information and attribute information fusion, prior information integration and over-lapping community detection. Aiming at these problems, in this paper we devise a semi -supervised overlapping community detection method named SSGCAE which is based on graph neural networks. This method is composed of three modules: graph convolutional autoencoder (GCAE), semi-supervision and modularity maximization, which are respec-tively utilized to fuse link information and attribute information, integrate prior informa-tion and detect overlapping communities. We treat GCAE as the backbone framework and train it by using the unified loss from these three modules. Through this way, these three modules are jointly correlated via the community membership representation, which is very beneficial to improve the overall performance. SSGCAE is comprehensively evaluated on synthetic and real attributed graphs, and experiment results show that it is very effec-tive and outperforms state-of-the-art baseline approaches.(c) 2022 Elsevier Inc. All rights reserved.

关键词： overlapping community detection Semi -supervised learning Graph convolutional autoencoder Graph neural networks Attributed graph

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Combining Non-negative Matrix Factorization and Sparse Coding for Functional Brain overlapping community detection

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COGNITIVE COMPUTATION 2018年第6期10卷 991-1005页

作者： Li, X. Hu, Z. Wang, H. Foshan Univ Sch Math & Big Data Foshan 528000 Guangdong Peoples R China Southeast Univ Res Ctr Learning Sci Nanjing 210096 Jiangsu Peoples R China Anhui Univ Technol Sch Math & Phys Maanshan 243002 Anhui Peoples R China

The functional system of the human brain can be viewed as a complex network. Among various features of the brain functional network, community structure has raised significant interest in recent years. Increasing evidence has revealed that most realistic complex networks have an overlapping community structure. However, the overlapping community structure of the brain functional network has not been adequately studied. In this paper, we propose a novel method called sparse symmetric non-negative matrix factorization (ssNMF) to detect the overlapping community structure of the brain functional network. Specifically, it is formulated by combining the effective techniques of non-negative matrix factorization and sparse coding. Besides, the non-negative adaptive sparse representation is applied to construct the whole-brain functional network, based on which ssNMF is performed to detect the community structure. Both simulated and real functional magnetic resonance imaging data are used to evaluate ssNMF. The experimental results demonstrate that the proposed ssNMF method is capable of accurately and stably detecting the underlying overlapping community structure. Moreover, the physiological interpretation of the overlapping community structure detected by ssNMF is straightforward. This novel framework, we think, provides an effective tool to study overlapping community structure and facilitates the understanding of the network organization of the functional human brain.

关键词： overlapping community detection Non-negative matrix factorization Sparse coding Brain functional network Functional magnetic resonance imaging

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overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagation

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EXPERT SYSTEMS 2022年第5期39卷

作者： Jokar, Ehsan Mosleh, Mohammad Kheyrandish, Mohammad Islamic Azad Univ Dept Comp Engn Dezful Branch Dezful Iran

Complex networks represent various real-world systems. overlapping community detection is one of the critical tasks in studying these networks and has significance to a wide variety of applications, including the exploration of online social networks because of the natural attitude of persons to participate in multiple communities at the same time. Despite a large number of existing community detection algorithms for detecting disjoint communities, the efficient and fast uncovering of overlapping communities has remained a challenging problem. To provide an efficient solution, on the one hand, the balanced link density label propagation (BLDLP) algorithm, proposed by the authors of the current study, is a fast, stable, and efficient method for disjoint community detection. On the other hand, the fuzzy theory is a worthwhile approach for overlapping community detection since it provides the membership rate of the overlapping nodes as well as the detection of overlapping communities. Hence, in this paper, based on the synergy of the BLDLP algorithm and the fuzzy theory, a novel method, called fuzzy BLDLP, for overlapping community detection is proposed. Fuzzy BLDLP is fast and efficient. The proposed method needs no prior information about the number of network communities to discover them. The experiments on both synthetic and real-world known networks, including Zachary, Dolphins, and COVID-19 Co-authorship, have revealed that the proposed method successfully detects the overlapping nodes and communities and hence is comparable with the state-of-the-art overlapping community detection algorithms in terms of recall, precision, F-score and overlapping normalized mutual information.

关键词： complex networks fuzzy theory label propagation link density overlapping community detection social networks

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A novel two-step approach for overlapping community detection in social networks

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SOCIAL NETWORK ANALYSIS AND MINING 2017年第1期7卷 1-11页

作者： Sarswat, Aparna Jami, Vidyadhari Guddeti, Ram Mohana Reddy Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

With the rapid increase in popularity of online social networks, community detection in these networks has become a key aspect of research field. overlapping community detection is an important NP-hard problem of social network analysis. Modularity-based community detection is one of the most widely used approaches for social network analysis. However, modularity-based community detection technique may fail to resolve small-size communities. Hence, we propose a novel two-step approach for overlapping community detection in social networks. In the first step, modularity density-based hybrid meta-heuristics approach is used to find the disjoint communities and the quality of these disjoint communities can be verified using Silhouette coefficient. In the second step, the quality disjoint communities with low computation cost are used to detect overlapping nodes based on Min-Max Ratio of minimum(indegree, outdegree) to the maximum(indegree, outdegree) values of nodes. We tested the proposed algorithm based on 10 standard community quality metrics along with Silhouette score using seven standard datasets. Experimental results demonstrate that the proposed approach outperforms the current state-of-the-art works in terms of quality and scalability.

关键词： overlapping community detection Disjoint community detection Modularity density Hybrid bio-inspired algorithms Silhouette coefficient

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overlapping community detection EXTENDED FROM DISJOINT community STRUCTURE

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COMPUTING AND INFORMATICS 2019年第5期38卷 1091-1110页

作者： Xing, Yan Meng, Fanrong Zhou, Yong Sun, Guibin Wang, Zhixiao Civil Aviat Univ China Sch Comp Sci & Technol Tianjin 300300 Peoples R China China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China

community detection is a hot issue in the study of complex networks. Many community detection algorithms have been put forward in different fields. But most of the existing community detection algorithms are used to find disjoint community structure. In order to make full use of the disjoint community detection algorithms to adapt to the new demand of overlapping community detection, this paper proposes an overlapping community detection algorithm extended from disjoint community structure by selecting overlapping nodes (ONS-OCD). In the algorithm, disjoint community structure with high qualities is firstly taken as input, then, potential members of each community are identified. overlapping nodes are determined according to the node contribution to the community. Finally, adding overlapping nodes to all communities they belong to and get the final overlapping community structure. ONS-OCD algorithm reduces the computation of judging overlapping nodes by narrowing the scope of the potential member nodes of each community. Experimental results both on synthetic and real networks show that the community detection quality of ONS-OCD algorithm is better than several other representative overlapping community detection algorithms.

关键词： Disjoint community detection overlapping community detection potential member overlapping node

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A semi-orthogonal nonnegative matrix tri-factorization algorithm for overlapping community detection

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STATISTICAL PAPERS 2024年第6期65卷 3601-3619页

作者： Li, Zhaoyang Yang, Yuehan Fudan Univ Sch Publ Hlth Dept Biostat Shanghai 200433 Peoples R China Cent Univ Finance & Econ Sch Stat & Math Beijing 100081 Peoples R China

In this paper, we focus on overlapping community detection and propose an efficient semi-orthogonal nonnegative matrix tri-factorization (semi-ONMTF) algorithm. This method factorizes a matrix X into an orthogonal matrix U, a nonnegative matrix B, and a transposed matrix UT\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U<^>\mathrm {\scriptscriptstyle T} $$\end{document}. We use the Cayley Transformation to maintain strict orthogonality of U that each iteration stays on the Stiefel Manifold. This algorithm is computationally efficient because the solutions of U and B are simplified into a matrix-wise update algorithm. Applying this method, we detect overlapping communities by the belonging coefficient vector and analyse associations between communities by the unweighted network of communities. We conduct simulations and applications to show that the proposed method has wide applicability. In a real data example, we apply the semi-ONMTF to a stock data set and construct a directed association network of companies. Based on the modularity for directed and overlapping communities, we obtain five overlapping communities, 17 overlapping nodes, and five outlier nodes in the network. We also discuss the associations between communities, providing insights into the overlapping community detection on the stock market network.

关键词： Semi-ONMTF overlapping community detection Cayley transformation Matrix-wise update algorithm Stock market

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overlapping community detection based on link graph using distance dynamics

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2018年第3期32卷

作者： Chen, Lei Zhang, Jing Cai, Li-Jun Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

关键词： overlapping community detection line graph interaction model complex network graph clustering

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A comparative study of overlapping community detection methods from the perspective of the structural properties

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APPLIED NETWORK SCIENCE 2020年第1期5卷 1-42页

作者： Vieira, Vinicius da Fonseca Xavier, Carolina Ribeiro Evsukoff, Alexandre Goncalves Univ Fed Sao Joao del Rei Sao Joao Del Rei MG Brazil Univ Fed Rio de Janeiro COPPE POB 68506 Rio De Janeiro RJ Brazil

community detection is one of the most important tasks in network analysis. It is increasingly clear that quality measures are not sufficient for assessing communities and structural properties play a key hole in understanding how nodes are organized in the network. This work presents a comparative study of some representative state-of-the-art methods for overlapping community detection from the perspective of the structural properties of the communities identified by them. Experiments with synthetic and real-world benchmark Ground-Truth networks show that, although the methods are able to identify modular communities, they often miss many structural properties of the communities, such as the number of nodes in the overlapping region and the memberships of the nodes. This is a strong suggestion that a deeper comprehension of the overlapping properties of the communities is needed for the design of more efficient community detection methods.

关键词： overlapping community detection Structural properties Comparison of methods

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Disassortative Degree Mixing and Information Diffusion for overlapping community detection in Social Networks (DMID) 24

Disassortative Degree Mixing and Information Diffusion for O...

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24th International Conference on World Wide Web (WWW)

作者： Shahriari, Mohsen Krott, Sebastian Klamma, Ralf Rhein Westfal TH Aachen Adv Community Informat Syst Ahornstr 55 D-52056 Aachen Germany

ISBN: (纸本)9781450334730

In this paper we propose a new two-phase algorithm for overlapping community detection (OCD) in social networks. In the first phase, called disassortative degree mixing, we identify nodes with high degrees through a random walk process on the row-normalized disassortative matrix representation of the network. In the second phase, we calculate how closely each node of the network is bound to the leaders via a cascading process called network coordination game. We implemented the algorithm and four additional ones as a Web service on a federated peer-to-peer infrastructure. Comparative test results for small and big real world networks demonstrated the correct identification of leaders, high precision and good time complexity. The Web service is available as open source software.

关键词： overlapping community detection expert identification information diffusion Web service

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