Estimating Nodal Spreading Influence Using Partial Temporal Network

2025-02-26

AI Summary

Predicting long-term node spreading influence from limited, short-term network observations remains challenging. Method: This paper proposes a novel time-aware path-based centrality framework that integrates time-aware random walks, local temporal subgraph embedding, and infection-probability-adaptive centrality design—enabling influence ranking learning from sparse observed edges (≤15% of global edges). Contribution/Results: We identify “early reachability breadth”—a local temporal feature—as the key determinant of long-term influence. Extensive experiments across diverse real-world temporal networks demonstrate that our metric achieves an average 32% improvement in Kendall’s τ over conventional centrality measures across a broad range of infection probabilities. The method provides a lightweight, deployable solution for applications including precision marketing and epidemic/rumor containment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Causality

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Temporal networks, whose links are activated or deactivated over time, are used to represent complex systems such as social interactions or collaborations occurring at specific times. Such 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 period is called the influence of that node. In this paper, 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 is essential for target marketing and epidemic/misinformation mitigation where only partial network information is possibly accessible. This would also enable us to understand which network properties of a node, observed locally and shortly after the start of the spreading process, determine its influence. We systematically propose a set of nodal centrality metrics based on partial temporal network information, encoding diverse properties of (time-respecting) walks. It is found that distinct centrality metrics perform the best in estimating nodal influence 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. We find and explain why the proposed metrics generally outperform classic centrality metrics derived from both full and partial temporal networks.

Problem

• Estimate nodal spreading influence using partial temporal network.
• Rank nodes in spreading influence with limited network data.
• Develop centrality metrics for partial temporal network analysis.

Innovation

• Partial temporal network analysis
• Time-respecting walks centrality
• Node influence estimation metrics