Influential Node Detection on Graph on Event Sequence

Conference paper (2024)

Authors

Zehao Lu Universiteit Utrecht
Shihan Wang Universiteit Utrecht
Xiao Long Ren University of Electronic Science and Technology of China
Rodrigo Costas Universiteit Leiden
T.A.P. Metze Organisation & Governance - TPM
Research Group
Organisation & Governance (TPM) (TU Delft)
Copyright: © 2024 Zehao Lu, Shihan Wang, Xiao Long Ren, Rodrigo Costas, T.A.P. Metze
DOI: https://doi.org/10.1007/978-3-031-53472-0_13
Dynamics of Network Influential Node Detection Non-epidemic Spreading
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Published Date

2024

Language

English

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Faculty

Technology, Policy and Management

Department

Multi Actor Systems

Research Group

Organisation & Governance

Abstract

Numerous research efforts have centered on identifying the most influential players in networked social systems. This problem is immensely crucial in the research of complex networks. Most existing techniques either model social dynamics on static networks only and ignore the underlying time-serial nature or model the social interactions as temporal edges without considering the influential relationship between them. In this paper, we propose a novel perspective of modeling social interaction data as the graph on event sequence, as well as the Soft K-Shell algorithm that analyzes not only the network’s local and global structural aspects, but also the underlying spreading dynamics. The extensive experiments validated the efficiency and feasibility of our method in various social networks from real world data. To the best of our knowledge, this work is the first of its kind.