Online Social Network Information Source Identification Algorithm Based on Multi-Attribute Topological Clustering

Author:

Dong Ming1ORCID,Lu Yujuan2,Tan Zhenhua2ORCID,Zhang Bin2

Affiliation:

1. School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

2. Software College, Northeastern University, Shenyang 110819, China

Abstract

This paper focuses on the problem of information source identification in online social networks (OSNs). By analyzing the research situation of source identification problems and challenges (such as the randomness of the information dissemination process and complexity of the underlying network topology), this paper studies the problem of multiple source diffusion and proposes a source identification algorithm based on multi-attribute topological clustering (MaTC). The basic idea of the algorithm is to decompose the multi-source problems into a series of single-source problems by using clustering partitioning to improve accuracy and efficiency. Firstly, it estimates the number of source nodes, which is also the number of network partitions, then characterizes the combination of multiple attribute structures as an attribute index of topological clustering, performs an analysis of the distribution of real source nodes in each partition to evaluate the accuracy of the clustering partition, and finally uses Jordan centrality within each partition for single-source identification. Through comparative experiments, it is verified that the proposed MaTC algorithm is superior to the comparison algorithms in evaluating indicators.

Funder

the National Key Research and Development Program of China under Grant

Publisher

MDPI AG

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