Identifying important nodes based on upstream and downstream time-respecting paths in temporal networks

Luo, L, Li, M, Zhang, Zili and Tao, L 2021, Identifying important nodes based on upstream and downstream time-respecting paths in temporal networks, Modern Physics Letters B, vol. 35, no. 23, doi: 10.1142/S0217984921504030.

Title Identifying important nodes based on upstream and downstream time-respecting paths in temporal networks
Author(s) Luo, L
Li, M
Zhang, ZiliORCID iD for Zhang, Zili
Tao, L
Journal name Modern Physics Letters B
Volume number 35
Issue number 23
Article ID 2150403
Publisher World Scientific Pub. Co.
Place of publication Singapore
Publication date 2021-08-20
ISSN 0217-9849
Keyword(s) Temporal social networks
important node identification
time-respecting paths
upstream–downstream centrality
susceptible-infected model
Summary Identifying the nodes that play significant roles in the epidemic spreading process has attracted extensive attention in recent years. Few centrality measures, such as temporal degree and temporal closeness centrality, have been proposed to quantify node importance based on the topological structure of social contact networks. Most methods estimate the importance of a node from a single aspect, e.g. a higher degree in time snapshot graphs, or shorter distances to other nodes along time-respecting paths. However, this may not be the case in the real world. On the one hand, a node with more nodes on its out streams (i.e. downstream) should be more important because it may affect more nodes along its time-stamped contacting paths once it is infected. On the other hand, a node with more nodes in its in streams (i.e. upstream) deserves closer attention, as it has a higher probability of infection by other nodes. We propose a new temporal centrality measure, upstream and downstream centrality (UD-centrality) with two forms of realizations, i.e. a linear UD-centrality (L-UD) and a product UD-centrality (P-UD) to estimate the importance of nodes based on the temporal structures of social contact networks. We compare our L-UD and P-UD to three classic temporal network centralities through simulations on 14 real-world temporal networks based on the susceptible-infected (SI) model. The comparison results show that UD-centrality can more accurately rank the importance of nodes than the baseline centrality measures.
Language eng
DOI 10.1142/S0217984921504030
Field of Research 01 Mathematical Sciences
02 Physical Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
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