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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 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 |
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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.
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Received: 07 April 2022
Revised: 21 July 2022
Accepted manuscript online: 01 September 2022
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PACS:
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89.75.Fb
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(Structures and organization in complex systems)
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| Fund: 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). |
Corresponding Authors:
Lai-Jun Zhao
E-mail: ljzhao@usst.edu.cn
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Cite this article:
Ping-Le Yang(杨平乐), Lai-Jun Zhao(赵来军), Chen Dong(董晨),Gui-Qiong Xu(徐桂琼), and Li-Xin Zhou(周立欣) AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality 2023 Chin. Phys. B 32 058901
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