TY - GEN
T1 - Influential Node Detection on Graph on Event Sequence
AU - Lu, Zehao
AU - Wang, Shihan
AU - Ren, Xiao Long
AU - Costas, Rodrigo
AU - Metze, Tamara
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2024
Y1 - 2024
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.
KW - Dynamics of Network
KW - Influential Node Detection
KW - Non-epidemic Spreading
UR - http://www.scopus.com/inward/record.url?scp=85186724203&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-53472-0_13
DO - 10.1007/978-3-031-53472-0_13
M3 - Conference contribution
AN - SCOPUS:85186724203
SN - 9783031534713
T3 - Studies in Computational Intelligence
SP - 147
EP - 158
BT - Complex Networks and Their Applications XII - Proceedings of The Twelfth International Conference on Complex Networks and their Applications
A2 - Cherifi, Hocine
A2 - Rocha, Luis M.
A2 - Cherifi, Chantal
A2 - Donduran, Murat
PB - Springer
T2 - 12th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2023
Y2 - 28 November 2023 through 30 November 2023
ER -