Identifying Multiple Influential Spreaders in Complex Networks by Considering the Dispersion of Nodes

Frontiers in Physics

Mutong Liu ¹  Zili Zhang ²  Li Tao ²  Liang Luo ² 

[1] Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong, SAR, China;School of Computer and Information Science, Southwest University, Chongqing, China;

关键词: identification of multiple influential spreaders;    dispersion of nodes;    location index rank algorithm;    independent set algorithm;    susceptible-infected-recovered model;   

DOI  :  10.3389/fphy.2021.766615

来源: Frontiers

【 摘 要 】

Identifying multiple influential spreaders, which relates to finding k (k > 1) nodes with the most significant influence, is of great importance both in theoretical and practical applications. It is usually formulated as a node-ranking problem and addressed by sorting spreaders’ influence as measured based on the topological structure of interactions or propagation process of spreaders. However, ranking-based algorithms may not guarantee that the selected spreaders have the maximum influence, as these nodes may be adjacent, and thus play redundant roles in the propagation process. We propose three new algorithms to select multiple spreaders by taking into account the dispersion of nodes in the following ways: (1) improving a well-performed local index rank (LIR) algorithm by extending its key concept of the local index (an index measures how many of a node’s neighbors have a higher degree) from first-to second-order neighbors; (2) combining the LIR and independent set (IS) methods, which is a generalization of the coloring problem for complex networks and can ensure the selected nodes are non-adjacent if they have the same color; (3) combining the improved second-order LIR method and IS method so as to make the selected spreaders more disperse. We evaluate the proposed methods against six baseline methods on 10 synthetic networks and five real networks based on the classic susceptible-infected-recovered (SIR) model. The experimental results show that our proposed methods can identify nodes that are more influential. This suggests that taking into account the distances between nodes may aid in the identification of multiple influential spreaders.

【 授权许可】

CC BY