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An improved game-theoretic approach to uncover overlapping communities

Author

Listed:
  • Hong-Liang Sun

    (NVIDIA Joint-Lab on Mixed Reality, International Doctoral Innovation Centre, The University of Nottingham, Ningbo 315100, P. R. China)

  • Eugene Ch’ng

    (NVIDIA Joint-Lab on Mixed Reality, International Doctoral Innovation Centre, The University of Nottingham, Ningbo 315100, P. R. China)

  • Xi Yong

    (Water Information Centre, Ministry of Water Resources, Beijing 100053, P. R. China)

  • Jonathan M. Garibaldi

    (School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK)

  • Simon See

    (NVIDIA AI Technology Centre, NVIDIA, Singapore 138522, Singapore5Centre for High Performance Computing, Shanghai Jiao Tong University, Shanghai 200240, P. R. China)

  • Duan-Bing Chen

    (Web Sciences Center, Big Data Research Center, The Center for Digitized Culture and Media, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China)

Abstract

How can we uncover overlapping communities from complex networks to understand the inherent structures and functions? Chen et al. firstly proposed a community game (Game) to study this problem, and the overlapping communities have been discovered when the game is convergent. It is based on the assumption that each vertex of the underlying network is a rational game player to maximize its utility. In this paper, we investigate how similar vertices affect the formation of community game. The Adamic–Adar Index (AA Index) has been employed to define the new utility function. This novel method has been evaluated on both synthetic and real-world networks. Experimental study shows that it has significant improvement of accuracy (from 4.8% to 37.6%) compared with the Game on 10 real networks. It is more efficient on Facebook networks (FN) and Amazon co-purchasing networks than on other networks. This result implicates that “friend circles of friends” of Facebook are valuable to understand the overlapping community division.

Suggested Citation

  • Hong-Liang Sun & Eugene Ch’ng & Xi Yong & Jonathan M. Garibaldi & Simon See & Duan-Bing Chen, 2017. "An improved game-theoretic approach to uncover overlapping communities," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(09), pages 1-17, September.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:09:n:s0129183117501121
    DOI: 10.1142/S0129183117501121
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    References listed on IDEAS

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    1. Newman, S. & Leventhal, T. & Gennetian, L.A., 2010. "Meeting children's basic needs: Introduction," Children and Youth Services Review, Elsevier, vol. 32(9), pages 1133-1137, September.
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    Cited by:

    1. Aghaalizadeh, Saeid & Afshord, Saeid Taghavi & Bouyer, Asgarali & Anari, Babak, 2021. "A three-stage algorithm for local community detection based on the high node importance ranking in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    2. Sun, Hong-liang & Chen, Duan-bing & He, Jia-lin & Ch’ng, Eugene, 2019. "A voting approach to uncover multiple influential spreaders on weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 303-312.
    3. Hesamipour, Sajjad & Balafar, Mohammad Ali, 2019. "A new method for detecting communities and their centers using the Adamic/Adar Index and game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    4. Kong, Hanzhang & Kang, Qinma & Li, Wenquan & Liu, Chao & Kang, Yunfan & He, Hong, 2019. "A hybrid iterated carousel greedy algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    5. Byungun Yoon & Songhee Kim & Sunhye Kim & Hyeonju Seol, 2022. "Doc2vec-based link prediction approach using SAO structures: application to patent network," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5385-5414, September.
    6. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.

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