communitydetection 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 lear...
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communitydetection 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 communitydetection, 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.
communitydetection 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 ide...
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communitydetection 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 communitydetection 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 communitydetection. CDGAAE is comprehensively evaluated on multiple real and synthetic datasets, and experimental results show that CDGAAE outperforms other competing methods.
The issue of network communitydetection has been extensively studied across many fields. Most communitydetection methods assume that nodes belong to only one community. However, in many cases, nodes can belong to mu...
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The issue of network communitydetection has been extensively studied across many fields. Most communitydetection 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 communitydetection 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.
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 repre...
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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.
communitydetection has long been designed to find communities with different structures in various networks. It is now widely believed that these communities often overlap with each other. However, due to the complex...
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communitydetection has long been designed to find communities with different structures in various networks. It is now widely believed that these communities often overlap with each other. However, due to the complexity and diversity of the network, it is often difficult to accurately identify the overlappingcommunity structure in many real networks. Considering the above problem, we introduce a dual graph neural network for overlapping community detection (DGOCD) under the framework of the extended Bernoulli-Poisson. First, we build two graphs to model information of different orders between nodes, respectively, and use a set of GCNs as a backbone to learn semantic representations of the above graphs in parallel. Then we introduce the concept of topological potential matrix to aggregate the embedding representations of the two channel graphs. Moreover, for learning the affiliations between nodes and communities, we carry out network reconstruction based on the former information. Finally, the reconstructed network is sent into the GCN to get the final community division. Experimental results on real network datasets demonstrate that the proposed DGOCD consistently outperforms existing methods.
In recent years, extensive studies have been carried out in communitydetection for social network analysis because it plays a crucial role in social network systems in today's world. However, most social networks...
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In recent years, extensive studies have been carried out in communitydetection for social network analysis because it plays a crucial role in social network systems in today's world. However, most social networks in the real world have complex overlapping social structures, one of the NP-hard problems. This paper presents a new model for overlapping community detection that uses a multi-objective approach based on a hybrid optimization algorithm. In this model, the Modified Selection Function (MSF) hybrids the algorithms and recovery mechanism, the Slime Mould Algorithm (SMA), the Sine Cosine Algorithm (SCA), and the association strategy. Also, considering that these algorithms have been presented to solve single-objective optimization problems, the Pareto dominance technique has been used to solve multi-objective problems. In addition to overlapping community detection and increasing detection accuracy, the fuzzy clustering technique has been used to select the heads of clusters. Sixteen synthetic and real-world data sets were utilized to assess the suggested model, and the outcomes were contrasted with those of existing optimization techniques. The proposed model has performed better than the other tested algorithms in comparing the tests conducted by us in all 16 data sets, in the comparisons made with the algorithms proposed in other works in 11 data sets out of 14 data. The set has performed better than competitors. As a conclusion, the findings show that this model performs better than other methods.
作者：
Chen, GaolinZhou, ShumingFujian 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...
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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.
An inherent limitation of many popular communitydetection 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 ...
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An inherent limitation of many popular communitydetection 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.
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 mat...
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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.
The widespread domain of social network analysis leads to numerous research challenges associated with it. communitydetection is one of the foremost research challenges. There are several communitydetection methods ...
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The widespread domain of social network analysis leads to numerous research challenges associated with it. communitydetection is one of the foremost research challenges. There are several communitydetection methods available in literature whose effectiveness for detecting communities has been analysed through evaluation of various metrics. But this criteria of empirical analysis for predicting performance of particular communitydetection method, needs to be further explored and refined. Major challenge with earlier surveys on empirical analysis of overlapping community detection methods is the lack of multidimensional framework for depicting the results. In literature, majority of analysis have been done by considering performance metrics only. Unlike other empirical analysis represented in literature, this paper emphasizes on analysis of interdependencies among various fitness metrics while detecting communities. Co-performance analysis based on partition comparison of overlapping community detection methods is also presented. The evaluation has been performed on real as well as benchmark datasets. This article can serve as a reference work for researchers in selection of particular overlapping community detection algorithm based on the analysis of partition comparison and inter-dependencies among fitness metrics.
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