Dynamics of temporal influence in polarised networks

2025-07-22

AI Summary

To address the challenges of modeling time-varying user influence and identifying cross-community key propagators in polarized social networks, this paper proposes a community-aware temporal centrality framework. Methodologically, it introduces an enhanced temporal degree centrality metric and develops a temporal independent cascade propagation model that explicitly incorporates opinion drift and participation decay. Crucially, it innovates an “influence hierarchy band” mechanism to enable hierarchical tracking and aggregation of node-level influence across communities. Experimental evaluations on multiple real-world polarized networks demonstrate that the framework consistently discriminates influence hierarchies and significantly improves the accuracy of identifying cross-community influential spreaders. The approach offers an interpretable and scalable computational paradigm for analyzing information diffusion dynamics and guiding influence-aware governance in polarized environments.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

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

Abstract

In social networks, it is often of interest to identify the most influential users who can successfully spread information to others. This is particularly important for marketing (e.g., targeting influencers for a marketing campaign) and to understand the dynamics of information diffusion (e.g., who is the most central user in the spreading of a certain type of information). However, different opinions often split the audience and make the network polarised. In polarised networks, information becomes soiled within communities in the network, and the most influential user within a network might not be the most influential across all communities. Additionally, influential users and their influence may change over time as users may change their opinion or choose to decrease or halt their engagement on the subject. In this work, we aim to study the temporal dynamics of users' influence in a polarised social network. We compare the stability of influence ranking using temporal centrality measures, while extending them to account for community structure across a number of network evolution behaviours. We show that we can successfully aggregate nodes into influence bands, and how to aggregate centrality scores to analyse the influence of communities over time. A modified version of the temporal independent cascade model and the temporal degree centrality perform the best in this setting, as they are able to reliably isolate nodes into their bands.

Problem

• Identify influential users in polarized social networks
• Study temporal dynamics of user influence changes
• Compare stability of influence ranking methods

Innovation

• Temporal centrality measures for influence ranking
• Modified temporal independent cascade model
• Aggregating centrality scores for community influence