A Coalition Formation Game Theory-Based Approach for Detecting Communities in Multi-relational Networks

Zhou, Lihua; Yang, Pei; Zhang, Zidong; Chen, Hongmei

doi:10.1007/978-3-319-21042-1_3

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…They proposed two approaches that based on Myerson value and Hedonic game, respectively. Zhou et al proposed cooperative game-based methods for identifying overlapping and hierarchical communities [27], and for detecting communities in multirelational networks [28].…”

Section: Game Theory For Community Detectionmentioning

confidence: 99%

Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory

et al. 2021

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Presently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving network topology properties in learned low-dimension representations. Community structure is a prominent characteristic of complex networks and thus should be well maintained. However, the difficulty lies in the fact that the properties of community structure are multivariate and complicated; therefore, it is insufficient to model community structure using a predefined model, the way that is popular in most state-of-the-art network embedding algorithms explicitly considering community structure preservation. In this paper, we introduce a multi-process parallel framework for network embedding that is enhanced by found partial community information and can preserve community properties well. We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. A series of experiments are conducted to evaluate the performance of our methods and six state-of-the-art algorithms. The results demonstrate that our methods can effectively preserve community properties of networks in their low-dimension representations. Specifically, compared to the involved baselines, our algorithms behave the best and are the runners-up on networks with high overlapping diversity and density.

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Section: Game Theory For Community Detectionmentioning

confidence: 99%

Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory

et al. 2021

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“…It is deterministic to generate the same result for a given work. Zhou presented a coalitional game for community detection in multi-relational social networks [20]. They used a greedy agglomerative algorithm to merge the coalitions to improve the utility of coalition.…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Game Theory-Based Algorithm for Overlapping Community Detection

2020

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Community detection is of great significance and has more guiding for network analysis which has attract a lot of attention and is widely used in many areas. The process of community formation is modeled as a game in this paper, nodes in networks are taken as players, and communities are regarded as coalitions. An overlapping community detection algorithm is presented based on cooperative game theory. Furthermore, an edge weight computation is proposed for calculating Shapley value of node and coalition, and a strategy of optimal income for the individual is designed. It is for each player to choose its best coalition to stay or leave during the game process. Through choosing coalitions which can improve the value of their characteristic function, coalitions can be merged to avoid small size coalitions. It does not need any prior information about the number and the size of communities and the overlapping community structure is stable at the end of the algorithm. The experimental results both on synthetic and real networks demonstrate the good performance of the proposed method. It can detect overlapping communities more accurately. INDEX TERMS Cooperative game, Shapley value, overlapping community detection.

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Multivariate Time Series Clustering via Multi-relational Community Detection in Networks

Zhou

Wang

et al. 2018

Web and Big Data

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A Coalition Formation Game Theory-Based Approach for Detecting Communities in Multi-relational Networks

Cited by 6 publications

References 29 publications

Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory

Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory

A Cooperative Game Theory-Based Algorithm for Overlapping Community Detection

Multivariate Time Series Clustering via Multi-relational Community Detection in Networks

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