Multi-source detection based on neighborhood entropy in social networks

Yan Xia Liu, Wei Min Li, Chao Yang*, Jian Jia Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes, resulting in a poor source localization effect. In addition, most existing methods ignore the existence of multiple source nodes in the infected cluster and hard to identify the source nodes comprehensively. To solve these problems, we propose a new method about the multiple sources location with the neighborhood entropy. The method first defines the two kinds of entropy, i.e. infection adjacency entropy and infection intensity entropy, depending on whether neighbor nodes are infected or not. Then, the possibility of a node is evaluated by the neighborhood entropy. To locate the source nodes comprehensively, we propose a source location algorithm with the infected clusters. Other unrecognized source nodes in the infection cluster are identified by the cohesion of nodes, which can deal with the situation in the multiple source nodes in an infected cluster. We conduct experiments on various network topologies. Experimental results show that the two proposed algorithms outperform the existing methods.

Original languageEnglish
Article number5467
JournalScientific Reports
Volume12
Issue number1
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

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