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2024 | OriginalPaper | Buchkapitel

Influential Node Detection on Graph on Event Sequence

verfasst von : Zehao Lu, Shihan Wang, Xiao-Long Ren, Rodrigo Costas, Tamara Metze

Erschienen in: Complex Networks & Their Applications XII

Verlag: Springer Nature Switzerland

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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.

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Vorheriges Kapitel A Correction to the Heuristic Algorithm MinimalFlipSet to Balance Unbalanced Graphs

Nächstes Kapitel Decentralized Control Methods in Hypergraph Distributed Optimization

A pseudocode of the proposed Soft K-shell algorithm could be found in this paper’s repository, https://github.com/com3dian/SoftKShell.

For the soft k-shell model, the parameter of node properties \(\alpha \) is user-defined. In our conceptual framework, this value is assumed to be calculated using other machine learning techniques and given as the prior knowledge. Consequently, we do not delve into the methodologies for obtaining this parameter in the paper.

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Titel: Influential Node Detection on Graph on Event Sequence
verfasst von: Zehao Lu
Shihan Wang
Xiao-Long Ren
Rodrigo Costas
Tamara Metze
Verlag: Springer Nature Switzerland
Buch: Complex Networks & Their Applications XII
Print ISBN: 978-3-031-53471-3

Electronic ISBN: 978-3-031-53472-0

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53472-0_13

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