中国物理B ›› 2023, Vol. 32 ›› Issue (5): 58901-058901.doi: 10.1088/1674-1056/ac8e56

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AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

Ping-Le Yang(杨平乐)1, Lai-Jun Zhao(赵来军)1,†, Chen Dong(董晨)2, Gui-Qiong Xu(徐桂琼)2, and Li-Xin Zhou(周立欣)1   

  1. 1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
    2 Department of Information Management, School of Management, Shanghai University, Shanghai 200444, China
  • 收稿日期:2022-04-07 修回日期:2022-07-21 接受日期:2022-09-01 出版日期:2023-04-21 发布日期:2023-05-09
  • 通讯作者: Lai-Jun Zhao E-mail:ljzhao@usst.edu.cn
  • 基金资助:
    Project supported by the National Social Science Foundation of China (Grant Nos. 21BGL217 and 18AZD005) and the National Natural Science Foundation of China (Grant Nos. 71874108 and 11871328).

AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality

Ping-Le Yang(杨平乐)1, Lai-Jun Zhao(赵来军)1,†, Chen Dong(董晨)2, Gui-Qiong Xu(徐桂琼)2, and Li-Xin Zhou(周立欣)1   

  1. 1 Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
    2 Department of Information Management, School of Management, Shanghai University, Shanghai 200444, China
  • Received:2022-04-07 Revised:2022-07-21 Accepted:2022-09-01 Online:2023-04-21 Published:2023-05-09
  • Contact: Lai-Jun Zhao E-mail:ljzhao@usst.edu.cn
  • Supported by:
    Project supported by the National Social Science Foundation of China (Grant Nos. 21BGL217 and 18AZD005) and the National Natural Science Foundation of China (Grant Nos. 71874108 and 11871328).

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摘要： The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process. This problem finds many practical applications in numerous areas such as information dissemination, epidemic immunity, and viral marketing. However, most existing influence maximization algorithms are limited by the "rich-club" phenomenon and are thus unable to avoid the influence overlap of seed spreaders. This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy, named AIGCrank, to identify a set of influential seeds. Specifically, the gravity centrality jointly employs the neighborhood, network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed. We also present a recursive ranking strategy for identifying seed nodes one-by-one. Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.

关键词: influential nodes, influence maximization, gravity centrality, recursive ranking strategy

Abstract: The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process. This problem finds many practical applications in numerous areas such as information dissemination, epidemic immunity, and viral marketing. However, most existing influence maximization algorithms are limited by the "rich-club" phenomenon and are thus unable to avoid the influence overlap of seed spreaders. This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy, named AIGCrank, to identify a set of influential seeds. Specifically, the gravity centrality jointly employs the neighborhood, network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed. We also present a recursive ranking strategy for identifying seed nodes one-by-one. Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.

Key words: influential nodes, influence maximization, gravity centrality, recursive ranking strategy

中图分类号:  (Structures and organization in complex systems)

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