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A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks

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DATA & KNOWLEDGE ENGINEERING 2018年 113卷 155-170页

作者： Sattari, Mohammad Zamanifar, Kamran Univ Isfahan Dept Comp Engn Esfahan Iran

community detection in temporal social networks is an increasingly challenging subject in network analysis. The Label Propagation Algorithm (LPA) is a simple and fast approach for community detection in dynamic networks. However, it tends to generate monster communities which decrease the accuracy of community detection, especially in dynamic social networks. In this paper, we propose a modified LPA, called Spreading Activation Label Propagation Algorithm in order to solve the problem. This method assigns a property, called activation value, to each label, where pairs (label name, activation value) are propagated by spreading activation process and the LPA. Furthermore, this algorithm uses two weighting algorithms, where each of them corresponds to one variation of the proposed method. Here, the variations of the proposed method and other available methods on real and synthetic networks are implemented. Experimental results on both real and synthetic networks show that all variations of the proposed method detect communities more accurately compared to the benchmark methods while they are slower than these methods.

关键词： Spreading activation Label propagation algorithm overlapping community detection Dynamic social networks

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A fuzzy adaptive resonance theory inspired overlapping community detection method for online social networks

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KNOWLEDGE-BASED SYSTEMS 2016年 113卷 75-87页

作者： Raj, Ebin Deni Babu, L. D. Dhinesh VIT Univ Sch Informat Technol & Engn Vellore Tamil Nadu India

There has been a surge in the research of complex network analysis in the recent years. This paper engages with online social network, which is the most popular complex network in the modern world. Network communities help to understand the organization of real world networks. Accordingly, this paper proposes and validates a novel algorithm for overlapping community detection in online social networks. We focus on the stability-plasticity problem in complex networks and attempt to solve it using a Fuzzy Adaptive resonance theory inspired algorithm. The algorithm consists of two stages namely prediction stage and comparison stage. The proposed algorithms make use of network measures such as Edge betweenness, Betweenness centrality, and pair betweenness. The algorithm has been tested and compared with other algorithms using benchmark datasets, artificial datasets and real network datasets. The experimental results obtained were better than other overlapping community detection algorithms. The entropy of the proposed model has been evaluated using overlapping normalized information, omega index, F-score and the cumulative performance value is 2.42 out of 3, which is better than other community detection algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： overlapping community detection Stability-plasticity problem Social network analysis Complex networks Fuzzy adaptive resonance theory

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M-Link: a link clustering memetic algorithm for overlapping community detection

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MEMETIC COMPUTING 2020年第2期12卷 87-99页

作者： Gabardo, Ademir C. Berretta, Regina Moscato, Pablo Univ Newcastle Sch Elect Engn & Comp Callaghan NSW Australia

Graphs and networks are a useful abstraction to represent a wide range of systems. Sets of nodes that are more highly interconnected constitute a 'community'. community detection algorithms help to reveal a decomposition of a network in modules. These communities can overlap, and nodes can have several community memberships. We present M-Link, a memetic algorithm for overlapping community detection. It maximises an objective function called link partition density. The communities of edges obtained with this method naturally translate to overlapping communities of nodes. The method is based on local expansion and a specialised local search mechanism. Label propagation methods are used for initialising a multi-agent tertiary tree population structure. We use the normalised mutual information to evaluate the similarity between the known community structure in a collection of benchmark networks and the community structure detected by M-Link. The method outperforms other state-of-the-art algorithms for overlapping community detection and it has better accuracy and stability.

关键词： overlapping community detection Memetic algorithm Evolutionary algorithms

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A novel overlapping community detection strategy based on Core-Bridge seeds

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第6期15卷 2131-2147页

作者： Chen, Gaolin Zhou, Shuming Fujian Normal Univ Sch Comp & Cyber Secur XueFu South St Fuzhou 350117 Peoples R China Fujian Normal Univ Sch Math & Stat XueFu South St Fuzhou 350117 Peoples R China Fujian Normal Univ Ctr Appl Math Fujian Prov XueFu South St Fuzhou 350117 Peoples R China Fujian Normal Univ Key Lab Analyt Math & Applicat Minist Educ XueFu South St Fuzhou 350117 Peoples R China

The last decade has witnessed the advance of overlapping community detection based on local expansion. In this paper, we propose a novel local expanding-based overlapping community detection algorithm, denoted by Core and Bridge Seeds Extension, that aims to improve the quality of communities. Instead of the traditional approaches to select the cores of communities as seeds, a new Core-Bridge triplet strategy is suggested to select seeds to generate the initial backbone and framework of the community. In the optimization stage, a stepwise refinement approach is adopted to solve the issue of unreasonable division and unassigned node allocation. A merge index is designed to merge communities reasonably. The comparisons about the methods to improve accuracy of community numbers based on the known algorithms are also presented. Experimental results on synthetic networks and real networks show that our strategy outperforms the state-of-art algorithms in stability and effectiveness.

关键词： Social network overlapping community detection Core-Bridge Seed selection community optimization community merge

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A Maximal Clique Based Multiobjective Evolutionary Algorithm for overlapping community detection

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2017年第3期21卷 363-377页

作者： Wen, Xuyun Chen, Wei-Neng Lin, Ying Gu, Tianlong Zhang, Huaxiang Li, Yun Yin, Yilong Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Guangdong Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Engn Guilin 541004 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China Dongguan Univ Technol Sch Comp Sci & Network Secur Dongguan 523808 Peoples R China Shandong Univ Jinan 250100 Peoples R China

Detecting community structure has become one important technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can belong to only one community. However, in many real-world networks, communities are often overlapped with each other. Developing overlapping community detection algorithms thus becomes necessary. Along this avenue, this paper proposes a maximal clique based multiobjective evolutionary algorithm (MOEA) for overlapping community detection. In this algorithm, a new representation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme allows MOEAs to handle the overlapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm.

关键词： Clique-based representation maximal-clique graph multiobjective evolutionary algorithm (MOEA) overlapping community detection

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An enhanced multi-objective biogeography-based optimization for overlapping community detection in social networks with node attributes

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INFORMATION SCIENCES 2023年第1期622卷 903-929页

作者： Reihanian, Ali Feizi-Derakhshi, Mohammad-Reza Aghdasi, Hadi S. Arak Univ Fac Engn Dept Comp Engn Arak *** Iran Univ Tabriz Fac Elect & Comp Engn Dept Comp Engn Tabriz Iran Arak Univ Res Inst Artificial Intelligence Arak *** Iran

community detection is one of the most important and interesting issues in social network analysis. Most of the current community detection algorithms tend to find communities in social networks with just considering the topological structures of the networks. In recent years, simultaneously considering of nodes' attributes and topological structures of social networks in the process of community detection has attracted the attentions of many scholars, and this consideration has been recently used in some community detection methods to increase their efficiencies and to enhance their performances in finding mean-ingful and relevant communities. But the problem is that most of these methods tend to find non-overlapping communities, while many real-world networks include communities that often overlap to some extent. In order to solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based on multi-objective biogeography-based optimization (BBO), is proposed in this paper to automatically find overlapping communities in a social network with node attributes with synchronously considering the density of connections and the similarity of nodes' attributes in the network. In MOBBO-OCD, an extended locus-based adjacency representation called OLAR is introduced to encode and decode overlapping communities. Based on OLAR, a rank-based migration operator along with a novel two-phase mutation strategy and a new double-point crossover are used in the evo-lution process of MOBBO-OCD to effectively lead the population into the evolution path. In order to assess the performance of MOBBO-OCD, a new metric called alpha_SAEM is pro-posed in this paper, which is able to evaluate the goodness of both overlapping and non -overlapping partitions with considering the two aspects of node attributes and linkage structure. Quantitative evaluations, based on three extensive experiments on 14 real-life data sets with diverse characteristics, reveal that MOBBO-OCD achieves fav

关键词： overlapping community detection Node attributes Topological structure Biogeography-based optimization Multi-objective optimization

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A maximal-clique-based set-covering approach to overlapping community detection

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OPTIMIZATION LETTERS 2024年第6期18卷 1491-1510页

作者： Brusco, Michael J. Steinley, Douglas Watts, Ashley L. Florida State Univ Dept Business Analyt Informat Syst & Supply Chain Tallahassee FL 32306 USA Univ Missouri Dept Psychol Sci Columbia MO USA

An inherent limitation of many popular community detection methods, such as the walktrap and spin glass algorithms, is that they do not allow vertices to have membership in more than one community. Clique percolation remedies this limitation by allowing overlapping communities but does not necessarily produce solutions in accordance with the standard definition of 'community' (i.e., a dense subgraph of the network), often fails to assign all vertices to at least one community and presents formidable model selection challenges. In this paper, we propose a set-covering approach to overlapping community detection that enables overlapping communities to be assembled from maximal cliques, or from candidate communities formed from k-1 adjacent cliques. The promise of this new approach is demonstrated via comparison to clique percolation in a simulation experiment, as well as through application to an empirical psychological network.

关键词： overlapping community detection Set-covering Maximal cliques Clique percolation

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An overlapping community detection Approach Based on Deepwalk and Improved Label Propagation

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 311-321页

作者： Yu, Hongtao Ma, Ru Chao, Jinbo Zhang, Fuzhi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Hebei Peoples R China

Label propagation-based overlapping community detection algorithms have been widely used in complex networks due to their simplicity and efficiency. However, such algorithms need to randomly choose neighbor nodes and do not fully take the network's topology into consideration, resulting in low stability and accuracy. Aiming at this problem, we propose an overlapping community detection approach based on DeepWalk and the improved label propagation. We first use the DeepWalk model to learn the network's topology to obtain low-dimensional vector representations that reflect the spatial location of nodes and construct the weight matrix through vector dot product operation. Then, we design a label propagation algorithm with a preference selection strategy, which can obtain stable overlapping communities by exchanging information with fixed neighbors on the basis of preserving the nodes' own labels. The experimental results on the real network and synthetic datasets show that the proposed approach has better accuracy and stability than the baseline methods.

关键词： Complex networks Image edge detection Heuristic algorithms detection algorithms Thermal stability Lapping Clustering algorithms DeepWalk label propagation overlapping community detection preference selection strategy

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An ant colony based algorithm for overlapping community detection in complex networks

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2015年 427卷 289-301页

作者： Zhou, Xu Liu, Yanheng Zhang, Jindong Liu, Tuming Zhang, Di Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Jilin Peoples R China

community detection is of great importance to understand the structures and functions of networks. Overlap is a significant feature of networks and overlapping community detection has attracted an increasing attention. Many algorithms have been presented to detect overlapping communities. In this paper, we present an ant colony based overlapping community detection algorithm which mainly includes ants' location initialization, ants' movement and post processing phases. An ants' location initialization strategy is designed to identify initial location of ants and initialize label list stored in each node. During the ants' movement phase, the entire ants move according to the transition probability matrix, and a new heuristic information computation approach is redefined to measure similarity between two nodes. Every node keeps a label list through the cooperation made by ants until a termination criterion is reached. A post processing phase is executed on the label list to get final overlapping community structure naturally. We illustrate the capability of our algorithm by making experiments on both synthetic networks and real world networks. The results demonstrate that our algorithm will have better performance in finding overlapping communities and overlapping nodes in synthetic datasets and real world datasets comparing with state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Ant colony algorithm overlapping community detection Heuristic information Complex networks

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An overlapping community detection algorithm in complex networks based on information theory

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DATA & KNOWLEDGE ENGINEERING 2018年 117卷 183-194页

作者： Zhou, Hongfang Zhang, Yao Li, Jin Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian 710048 Shaanxi Peoples R China

In this paper, a new algorithm for overlapping community detection is proposed. First, we propose a node importance evaluation matrix to calculate the important degree for each node;second, we put forward the difference function to detect overlapping points in complex networks;finally, we use triangle principle to detect communities in complex networks. We adopt two measures of Normalized Mutual Information and Modularity to evaluate the algorithm. The experimental results show that our algorithm has a good performance on detecting overlapping community.

关键词： overlapping community detection Complex networks Clustering Data mining

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