Influential Node Detection on Graph on Event Sequence

Zehao Lu; Shihan Wang; Xiao-Long Ren; Rodrigo Costas; Tamara Metze

doi:10.1007/978-3-031-53472-0_13

Influential Node Detection on Graph on Event Sequence

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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.

Original language	English
Title of host publication	Complex Networks and Their Applications XII
Subtitle of host publication	Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3
Editors	Hocine Cherifi, Luis M. Rocha, Chantal Cherifi, Murat Donduran
Place of Publication	Cham
Publisher	Springer
Pages	147-158
Number of pages	12
Edition	1
ISBN (Electronic)	978-3-031-53472-0
ISBN (Print)	978-3-031-53471-3
DOIs	https://doi.org/10.1007/978-3-031-53472-0_13
Publication status	Published - 21 Feb 2024

Publication series

Name	Studies in Computational Intelligence
Publisher	Springer
Volume	1143
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Keywords

Dynamics of Network
Influential Node Detection
Non-epidemic Spreading

Access to Document

10.1007/978-3-031-53472-0_13

Influential Node Detection on Graph on Event SequenceFinal published version, 1.25 MBLicence: Taverne

Cite this

Lu, Z., Wang, S., Ren, X.-L., Costas, R., & Metze, T. (2024). Influential Node Detection on Graph on Event Sequence. In H. Cherifi, L. M. Rocha, C. Cherifi, & M. Donduran (Eds.), Complex Networks and Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3 (1 ed., pp. 147-158). (Studies in Computational Intelligence; Vol. 1143). Springer. https://doi.org/10.1007/978-3-031-53472-0_13

Lu, Zehao ; Wang, Shihan ; Ren, Xiao-Long et al. / Influential Node Detection on Graph on Event Sequence. Complex Networks and Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3. editor / Hocine Cherifi ; Luis M. Rocha ; Chantal Cherifi ; Murat Donduran. 1. ed. Cham : Springer, 2024. pp. 147-158 (Studies in Computational Intelligence).

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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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Lu, Z, Wang, S, Ren, X-L, Costas, R & Metze, T 2024, Influential Node Detection on Graph on Event Sequence. in H Cherifi, LM Rocha, C Cherifi & M Donduran (eds), Complex Networks and Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3. 1 edn, Studies in Computational Intelligence, vol. 1143, Springer, Cham, pp. 147-158. https://doi.org/10.1007/978-3-031-53472-0_13

Influential Node Detection on Graph on Event Sequence. / Lu, Zehao; Wang, Shihan; Ren, Xiao-Long et al.
Complex Networks and Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3. ed. / Hocine Cherifi; Luis M. Rocha; Chantal Cherifi; Murat Donduran. 1. ed. Cham: Springer, 2024. p. 147-158 (Studies in Computational Intelligence; Vol. 1143).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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PY - 2024/2/21

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N2 - 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.

AB - 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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Lu Z, Wang S, Ren XL, Costas R, Metze T. Influential Node Detection on Graph on Event Sequence. In Cherifi H, Rocha LM, Cherifi C, Donduran M, editors, Complex Networks and Their Applications XII: Proceedings of The Twelfth International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023, Volume 3. 1 ed. Cham: Springer. 2024. p. 147-158. (Studies in Computational Intelligence). doi: 10.1007/978-3-031-53472-0_13