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A review of clique-based overlapping community detection algorithms

KNOWLEDGE AND INFORMATION SYSTEMS 2022年第8期64卷 2023-2058页

作者： Gupta, Sumit Kumar Singh, Dhirendra Pratap Choudhary, Jaytrilok Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal 462001 MP India

detection of communities is one of the prominent characteristics of vast and complex networks like social networks, collaborative networks, and web graphs. In the modern era, new users get added to these complex networks, which results in an expansion of application-generated networks. Extracting relevant information from these large networks has become one of the most prominent research areas. community detection tries to reduce the application-generated graph into smaller communities in which nodes within the community are similar. Most of the recent proposals are focused on detecting overlapping communities in the network with higher accuracy. An integral issue in graph theory is the enumeration of cliques in a larger graph. As clique is a group of completely connected nodes which shows the explicit communities means these nodes share the same types of information. Clique-based community detection algorithm utilizing the clique property of the graph also identifies the implicit communities, which is not directly shown in the graph. Many overlapping community detection algorithms are proposed by researchers that rely on cliques. The goal of this paper is to offer a comparative analysis of clique-based community detection algorithms. This paper provides a pervasive survey on research works identifying the cliques in a network for detecting overlapping communities. We bring together most of the state-of-the-art clique-based community detection algorithms into a single article with their accessible benchmark data sets. It presents a detailed description of methods based on K-cliques, maximal cliques, and triad percolation methods and addresses these approaches' challenges. Finally, the comparative analysis of overlapping community detection methodologies is also reported.

关键词： Graph algorithm community detection algorithm Clique-based algorithm Maximal clique-based algorithm Clique percolation method Triad percolation method overlapping community detection algorithm

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Recent trends on community detection algorithms: A survey

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MODERN PHYSICS LETTERS B 2020年第35期34卷

作者： Gupta, Sumit Singh, Dhirendra Pratap Maulana Azad Natl Inst Technol Dept Comp Sci & Engn Bhopal Madhya Pradesh India

In today's world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate;applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories;non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected.

关键词： community detection social network graph partitioning graph clustering overlapping community detection algorithm non-overlapping community detection algorithm

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