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overlapping community detection using graph attention networks

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Sismanis, Konstantinos Potikas, Petros Souliou, Dora Pagourtzis, Aris Natl Tech Univ Athens Sch Elect & Comp Engn Polytecnheioupoli Athens 15780 Greece

community detection is a research area with increasing practical significance. Successful examples of its application are found in many scientific areas like social networks, recommender systems and biology. Deep learning has achieved many successes (Miotto et al., 2018;Voulodimos et al., 2018) on various graph related tasks and is recently used in the field of community detection, offering accuracy and scalability. In this paper, we propose a novel method called Attention overlapping community detection (AOCD) a method that incorporates an attention mechanism into the well-known method called Neural overlapping community detection (NOCD) (Shchur and G & uuml;nnemann, 2019) to discover overlapping communities in graphs. We perform several experiments in order to evaluate our proposed method's ability to discover ground truth communities. Compared to NOCD, increased performance is achieved in many cases.

关键词： overlapping community detection Representation learning GAT GCN GNN

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overlapping community detection in Vehicular Social Networks Based on Graph Attention Autoencoder

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SENSORS 2025年第8期25卷 2601-2601页

作者： Gu, Xiang Huang, Qiwei Yang, Jie Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

community detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network. community structures with overlapping relationships are identified through network topology and vehicle attribute information, thereby optimizing communication efficiency, supporting resource allocation, and enhancing privacy protection. However, most existing community detection methods focus on non-overlapping communities, usually only considering the topological structure of the network, and often ignoring the attribute information of nodes. To address these problems, this paper proposes a semi-supervised overlapping community detection method based on graph attention autoencoder (CDGAAE). The method consists of three key components: graph attention autoencoder module, modularity optimization enhancement module, and semi-supervised clustering module. First, the graph attention autoencoder module fuses topological information and node attribute information and encodes nodes using a graph attention mechanism. Second, the modularity optimization enhancement module effectively captures the structure of overlapping communities. Finally, the semi-supervised clustering module combines prior information to improve the accuracy of community detection. CDGAAE is comprehensively evaluated on multiple real and synthetic datasets, and experimental results show that CDGAAE outperforms other competing methods.

关键词： graph attention autoencoder overlapping community detection semi-supervised clustering vehicular social networks

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A novel approach for overlapping community detection in social networks based on the attraction

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 85卷

作者： Chi, Kuo Qu, Hui Fu, Ziheng Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Hainan Univ Lab & Equipment Adm Dept Haikou 570228 Peoples R China

The growing scale of networks makes the study of social networks increasingly difficult. overlapping community detection can both make the network easier to analyze and manage by detecting communities and better represent the intersection between communities. In this paper, a novel approach for overlapping community detection in social networks is proposed. First, the nodes with local maximum degree are selected from the global network to form initial communities. Next, if the attraction between a community and its surrounding node exceeds a set threshold, these nodes can be directly attracted to that community. Then repeat the above process iteratively until communities no longer change, and nodes that have not yet been divided into communities are regarded as overlapping nodes if they are attracted to two or more communities all greater than the set threshold. In addition, the membership of an overlapping nodes in a related community can be calculated by computing the ratio of the attraction of that community to the overlapping node to the sum of the attractions that the node has. Finally, experimental results on 4 synthetic networks and 6 real-world networks show that the proposed algorithm is effective in detecting overlapping communities and performs better compared to some existing algorithms.

关键词： Social networks overlapping community detection The attraction between nodes Membership of nodes to communities

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overlapping community detection ALGORITHMS USING MODULARITY AND THE COSINE

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ADVANCES IN COMPLEX SYSTEMS 2025年第3期28卷

作者： Do, Duy hieu Phan, Thi ha duong Vietnam Acad Sci & Technol Inst Math 18 Hoang Quoc Viet Hanoi Vietnam Vietnam Acad Sci & Technol Inst Math Int Ctr Res & Postgrad Training Math Ausp UNESCO 18 Hoang Quoc Viet Hanoi Vietnam

The issue of network community detection has been extensively studied across many fields. Most community detection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to multiple communities simultaneously. This paper presents two overlapping network community detection algorithms that build on the two-step approach, using the extended modularity and cosine function. The applicability of our algorithms extends to both undirected and directed graph structures. To demonstrate the feasibility and effectiveness of these algorithms, we conducted experiments using real data.

关键词： overlapping community detection directed graph random walk stationary distribution cosine similarity

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overlapping community detection Based on Information Dynamics

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IEEE ACCESS 2018年 6卷 70919-70934页

作者： Sun, Zejun Wang, Bin Sheng, Jinfang Yu, Zhongjing Shao, Junming Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Pingdingshan Univ Dept Network Ctr Pingdingshan 467000 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Big Data Res Ctr Chengdu 611731 Sichuan Peoples R China

Identifying overlapping communities is essential for analyzing network structures, exploring the interactions of groups, studying network functions, and obtaining insight into the dynamics of networks. Many algorithms have been proposed for detecting overlapping communities but identifying the intrinsic communities is still a non-trivial problem because of the difficulties with parameter tuning, user bias criteria, and the lack of ground truth information. In this paper, we propose a new model called OCDID (overlapping community detection based on Information Dynamics) to uncover the overlapping communities, which treats the network as a dynamical system that allows an individual to communicate and share information with its neighbors. The information flow in the network is controlled by the underlying topology structure (e.g., the community structure), and the community structure is also reflected by the information dynamics. overlapping nodes act as bridges between multiple communities and the information from multiple communities flows through these nodes. Thus, the overlapping nodes can be identified by analyzing the information flow among communities. In addition, we use the monotone convergence theorem to confirm the convergence of our model. Experiments based on synthetic and real-world networks demonstrate that in most cases, our proposed approach is superior to other representative algorithms in terms of the quality of overlapping community detection.

关键词： Complex network diffusion information dynamics overlapping community detection

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overlapping community detection based on node location analysis

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KNOWLEDGE-BASED SYSTEMS 2016年 105卷 225-235页

作者： Wang Zhi-Xiao Li Ze-chao Ding Xiao-fang Tang Jin-hui China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

As a novel overlapping community detection theory, topology potential has inspired many methods. However, these methods ignore the mass difference between nodes, leading to inaccurate topological potential values of nodes. Moreover, additional strategies are needed to determine the community affiliation of nodes, further complicating the process of community detection. In this paper, we propose a new overlapping community detection method based on node location analysis. In the proposed method, the PageRank algorithm is used to evaluate the node mass, and the community affiliation of nodes is determined based on their positions in the inherent peak-valley structure of the topology potential field. Experimental results show that the proposed method exhibits excellent performance on artificial and real-world networks and outperforms other topology-potential-based and most non-topology-potential based methods. (C) 2016 Elsevier B.V. All rights reserved.

关键词： overlapping community detection Topology potential Peak valley structure Node mass calculation Node location analysis

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overlapping community detection in rating-based social networks through analyzing topics, ratings and links

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PATTERN RECOGNITION 2018年 81卷 370-387页

作者： Reihanian, Ali Feizi-Derakhshi, Mohammad-Reza Aghdasi, Hadi S. Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

Owing to advances in information technology, online communications between people living in different parts of the world have considerably increased. The subsequent emergence of social networks helped this kind of communications to be further organized. One of the most important issues considered when analyzing these kinds of networks is community detection, in which a majority of studies tend to detect disjoint communities through analyzing linkages of networks. What this paper aims to achieve is to obtain overlapping communities in which the members have the same topics of interest, and where the strengths of connections between them are the consequence of their communications' content analysis. Consequently, we have hereby proposed a generic framework for overlapping community detection in social networks with special focus on rating-based social networks. This framework considers the information shared by the users (ratings), as well as their topics of interest, for the sake of finding meaningful communities. This will lead us to topical communities in which members are interested in the same topics, and the strengths of their relationships are directly based on the rate of their viewpoints' unity. Quantitative evaluations also reveal that the framework presented in this study achieves favorable results which are quite superior to the results of 3 other relevant frameworks in the literature. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： overlapping community detection Content analysis Topical community Semantic network Rating-based social networks

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overlapping community detection for count-value networks

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2019年第1期9卷 1页

作者： Yu, QianCheng Yu, ZhiWen Wang, Zhu Wang, XiaoFeng Wang, YongZhi Northwestern Polytech Univ Sch Comp Sci Dongxiang Rd Xian 710072 Peoples R China Northwestern Polytech Univ Shanxi Prov Key Lab Embedded Syst Dongxiang Rd Xian 710072 Peoples R China North Minzu Univ Coll Comp Sci Yinchuan Peoples R China Jilin Univ Coll Instruments Changchun Jilin Peoples R China

Detecting network overlapping community has become a very hot research topic in the literature. However, overlapping community detection for count-value networks that naturally arise and are pervasive in our modern life, has not yet been thoroughly studied. We propose a generative model for count-value networks with overlapping community structure and use the Indian buffet process to model the community assignment matrix Z;thus, provide a flexible nonparametric Bayesian scheme that can allow the number of communities K to increase as more and more data are encountered instead of to be fixed in advance. Both collapsed and uncollapsed Gibbs sampler for the generative model have been derived. We conduct extensive experiments on simulated network data and real network data, and estimate the inference quality on single variable parameters. We find that the proposed model and inference procedure can bring us the desired experimental results.

关键词： overlapping community detection Count-value networks Generative network model Nonparametric Bayesian model Indian buffet process Inference quality estimation

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overlapping community detection through an improved multi-objective quantum-behaved particle swarm optimization

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JOURNAL OF HEURISTICS 2015年第4期21卷 549-575页

作者： Li, Yangyang Wang, Yang Chen, Jing Jiao, Licheng Shang, Ronghua Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

community detection is one of the most important problems in the field of complex networks in recent years. The majority of present algorithms only find disjoint communities, however, community often overlap to some extent in many real-world networks. In this paper, an improved multi-objective quantum-behaved particle swarm optimization (IMOQPSO) based on spectral-clustering is proposed to detect the overlapping community structure in complex networks. Firstly, the line graph of the graph modeling the network is formed, and a spectral method is employed to extract the spectral information of the line graph. Secondly, IMOQPSO is employed to solve the multi-objective optimization problem so as to resolve the separated community structure in the line graph which corresponding to the overlapping community structure in the graph presenting the network. Finally, a fine-tuning strategy is adopted to improve the accuracy of community detection. The experiments on both synthetic and real-world networks demonstrate our method achieves cover results which fit the real situation in an even better fashion.

关键词： Multi-objective optimization Quantum-behaved particle swarm optimization Complex networks overlapping community detection Spectral method

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overlapping community detection Using Multiobjective Genetic Algorithm

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2020年第3期7卷 802-817页

作者： Kumar, Amit Barman, Debaditya Sarkar, Ritam Chowdhury, Nirmalya Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Visva Bharati Univ Dept Comp & Syst Sci Santini Ketan 731235 W Bengal India

community detection or identification problem has emerged as an important task in the field of network analysis. The problem becomes more challenging when the communities overlap each other. Since the community detection problem can be modeled as an optimization problem, the genetic algorithm (GA) can be used to develop a solution. Typically, overlapping community detection problem is a multiobjective optimization problem and there may exist some conflicts among these objectives. Therefore, a simple GA-based solution is not efficient for detecting overlapping communities in a social network. For this purpose, we have proposed an overlapping community detection method based on nondominated sorting GA (NSGA)-II having two objective functions. The first objective function maximizes internal link density within the community using fuzzy membership values while the second objective function minimizes the external link density of the communities. The initial population has been created based on the neighbors. A mutation operator has been proposed in this article to restrict the randomness in the chromosome. An updation operator has also been proposed to modify a chromosome after crossover and mutation operation. The proposed method can handle different network structures properly. In order to show the effectiveness, the proposed method has been compared with some popular methods on several real-world environments in terms of two performance measurement techniques, namely, generalized normalized mutual information (gNMI) and extended modularity (Q(ov)). The empirical results reveal that our proposed method has succeeded to obtain better gNMI and comparable extended modularity values.

关键词： Genetic algorithm (GA) multiobjective optimization nondominated sorting GA (NSGA)-II overlapping community detection Pareto optimal solutions

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