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Coritivity-based influence maximization in social networks

Author

Listed:
  • Wu, Yanlei
  • Yang, Yang
  • Jiang, Fei
  • Jin, Shuyuan
  • Xu, Jin

Abstract

Influence maximization problem is about finding a small set of nodes from the social network as seed set so as to maximize the range of information diffusion. In this paper, the theory of coritivity and method of finding core nodes in networks are introduced to deal with this problem. From the perspective of network structure, core nodes are the important ones to network connectivity and is a competitive measurement of node influence. By finding the core of the network through coritivity we can finally get the initial active nodes required in the influence maximization problem. We compare this method with other conventional node-selection approaches in USAir97 and HEPTH datasets. Experimental results demonstrate that: (a) the coritivity-based method achieves large influence spread in all the diffusion models we use, and (b) the proposed method converges fast in all cases we consider.

Suggested Citation

  • Wu, Yanlei & Yang, Yang & Jiang, Fei & Jin, Shuyuan & Xu, Jin, 2014. "Coritivity-based influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 467-480.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:467-480
    DOI: 10.1016/j.physa.2014.09.010
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    References listed on IDEAS

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    1. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
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    3. Hou, Bonan & Yao, Yiping & Liao, Dongsheng, 2012. "Identifying all-around nodes for spreading dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4012-4017.
    4. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
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    Cited by:

    1. Xiao, Yunpeng & Wang, Zheng & Li, Qian & Li, Tun, 2019. "Dynamic model of information diffusion based on multidimensional complex network space and social game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 578-590.
    2. Yan, Fuhan & Li, Zhaofeng & Jiang, Yichuan, 2016. "Controllable uncertain opinion diffusion under confidence bound and unpredicted diffusion probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 85-100.
    3. Zhao, Jiuhua & Liu, Qipeng & Wang, Lin & Wang, Xiaofan, 2017. "Competitive seeds-selection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 240-248.
    4. Wang, Qiyao & Jin, Yuehui & Lin, Zhen & Cheng, Shiduan & Yang, Tan, 2016. "Influence maximization in social networks under an independent cascade-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 20-34.

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