Estimating nodal spreading influence using partial temporal networks

Tianrui Mao, Shilun Zhang, Alan Hanjalic and Huijuan Wang

Chaos, Solitons & Fractals, 2025, vol. 201, issue P1

Abstract: Networks facilitate the spread of information and epidemics. The average number of nodes infected via a spreading process on a network starting from a single seed node over a given long period is called the influence of that node. Estimating nodal influence early in time is essential for the epidemic/misinformation mitigation. Influence estimation has been investigated in static networks, which identifies the relation between topological properties of a node and its influence and assumes the networks are completely known. However, the networks underlying spreading processes such as social interactions are not static but temporal networks, whose links are activated or deactivated over time. When predicting nodal influence in the long-term future, the temporal network is usually only observable till the time of prediction and only locally around the node due to data accessibility. To bridge this gap, we address the question of how to utilize the partially observed temporal network (local and of short duration) around each node, to estimate the ranking of nodes in spreading influence on the full network over a long period. This would also enable us to understand which network properties of a node, in its partially observed temporal network determine its influence. Centrality metrics (nodal properties) have been proposed recently in temporal networks. However, using such a metric derived for each node from its partial network to estimate the ranking of nodes in influence is likely to be limiting. This is because the spread of information is possibly through any time-respecting path, beyond the shortest time-respecting path considered by existing metrics. To address this disparity, we systematically propose a set of novel nodal centrality metrics that encode diverse properties of (time-respecting) walks to predict nodal influence rankings. The proposed metrics derived from partial network information, in general, outperform classic centrality metrics utilizing either full or partial temporal network information. It is found that distinct centrality metrics perform the best depending on the infection probability of the spreading process. For a broad range of the infection probability, a node tends to be influential if it can reach many distinct nodes via time-respecting walks and if these nodes can be reached early in time.

Keywords: Nodal influence estimation; Spreading processes; Walk-based centrality metrics; Nodal properties; Temporal networks; Partial network information (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925011762
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:201:y:2025:i:p1:s0960077925011762

DOI: 10.1016/j.chaos.2025.117163

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().