A Cooperative Game Theory-Based Algorithm for Overlapping Community Detection

Zhou, Xu; Cheng, Shichao; Liu, Yanheng

doi:10.1109/access.2020.2985397

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…With the aim of evaluating our proposed algorithm performance, we compare it with some other algorithms named GAME1 17 , GAME2 24 , GAME3 25 , SLPA 22 , OSLOM 26 , CPM 27 , GCE 28 and LFM 29 . GAME1 is based on non-cooperative game theory with the time complexity of O(m 2 ) .…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Detecting overlapping communities in complex networks using non-cooperative games

Ferdowsi

Samani

2022

Sci Rep

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Detecting communities in complex networks is of paramount importance, and its wide range of real-life applications in various areas has caused a lot of attention to be paid to it, and many efforts have been made to have efficient and accurate algorithms for this purpose. In this paper, we proposed a non-cooperative game theoretic-based algorithm that is able to detect overlapping communities. In this algorithm, nodes are regarded as players, and communities are assumed to be groups of players with similar strategies. Our two-phase algorithm detects communities and the overlapping nodes in separate phases that, while increasing the accuracy, especially in detecting overlapping nodes, brings about higher algorithm speed. Moreover, there is no need for setting parameters regarding the size or number of communities, and the absence of any stochastic process caused this algorithm to be stable. By appropriately adjusting stop criteria, our algorithm can be categorized among those with linear time complexity, making it highly scalable for large networks. Experiments on synthetic and real-world networks demonstrate our algorithm’s good performance compared to similar algorithms in terms of detected overlapping nodes, detected communities size distribution, modularity, and normalized mutual information.

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Section: Experimental Results and Comparisonmentioning

confidence: 99%

Detecting overlapping communities in complex networks using non-cooperative games

Ferdowsi

Samani

2022

Sci Rep

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show abstract

“…With the aim of evaluating our proposed algorithm performance, we compare it with some other algorithms named GAME1 13 , GAME2 21 , GAME3 22 , SLPA 19 , OSLOM 23 , CPM 24 , GCE 25 and LFM 26 . GAME1 is based on non-cooperative game theory with time complexity of O(m 2 ).…”

Section: Experimental Results and Comparisonmentioning

confidence: 99%

Detecting Overlapping Communities In Complex Networks Using Non-Cooperative Games

Ferdowsi

Samani

2022

Preprint

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Detecting communities in complex networks is of paramount importance, and its wide range of real-life applications in various areas has caused a lot of attention to be paid to it, and many efforts have been made to have efficient and accurate algorithms for this purpose. In this paper, we proposed a non-cooperative game theoretic-based algorithm that is able to detect overlapping communities. In this algorithm, nodes are regarded as players, and communities are assumed to be groups of players with similar strategies. Our two-phase algorithm detects communities and the overlapping nodes in separate phases that, while increasing the accuracy, especially in detecting overlapping nodes, brings about higher algorithm speed. Moreover, there is no need for setting parameters regarding the size or number of communities, and the absence of any stochastic process caused this algorithm to be stable. By appropriately adjusting stop criteria, our algorithm can be categorized among those with linear time complexity, making it highly scalable for large networks. Experiments on synthetic and real-world networks demonstrate our algorithm's good performance compared to similar algorithms in terms of detected overlapping nodes, detected communities size distribution, modularity, and normalized mutual information.

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“…Nodes were taken into account by Zhou et al [35] as players attempted to increase the utility of their coalitions by taking part in a cooperative games. In this study, an edge weight computation was proposed to determine the Shapley value for coalitions and nodes.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

Torkaman,

Badie,

Salajegheh

et al. 2023

IJE

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Today, with the proliferation of complex networks and their large amounts of data, researchers have great concerns about the accurate community detection methods. The difficulty in analyzing these networks stems from their enormous size and the complex relationships among the members of the networks. It is difficult to analyze the deep relationships and mechanisms by just looking at the whole. Traditional methods have some problems and limitations when analyzing these networks such as feature extraction, high reliance on the initial phase settings, computational complexity, neglect of network relationships and content. From the perspective of relationships and interactions between individuals, the environment of complex networks can be compared to a game in which nodes acting as players or agents may join or leave a community based on similar structural or semantic characteristics. Consequently, there is a strong tendency to use cooperative and non-cooperative games to detect communities. Moreover, the amalgamation of deep learning techniques and game theory has recently been proven to be highly effective in extracting communities. Deep learning techniques have demonstrated enhanced capability in feature engineering and automate the process. In this study, the authors make effort to detect rational and accurate communities based on structural and content features with the help of traditional approaches, deep learning, as well as cooperative and non-cooperative games. The efficiency of this study is demonstrated by experimental findings on real datasets, and confirming that it is able enough to identify those communities that are more meaningful.

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A Cooperative Game Theory-Based Algorithm for Overlapping Community Detection

Cited by 10 publications

References 22 publications

Detecting overlapping communities in complex networks using non-cooperative games

Detecting overlapping communities in complex networks using non-cooperative games

Detecting Overlapping Communities In Complex Networks Using Non-Cooperative Games

A Hybrid Approach to Detect Researchers’ Communities Based on Deep Learning and Game Theory

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