Evaluating Centrality-Based Seed Node Strategies for Influence Diffusion in OSNs: A Study across SCC, WCC and Full Networks using SIR, LT and IC Diffusion Models - Baabcha, Halima; Laifa, Meriem; Akhrouf, Samir (Informatica, 2025)
This study investigates influence diffusion in online social networks (OSNs) through a comprehensive analysis of centrality measures and diffusion models using the Higgs Twitter dataset. We model OSNs as directed graphs, focusing on strongly connected components (SCCs) and weakly connected components (WCCs). Seven centrality measures (out-degree, in-degree, betweenness, closeness, eigenvector, PageRank, and Katz centrality) are calculated to identify key influential nodes. The top-ranked nodes are then subjected to influence diffusion simulations using three models: Linear Threshold (LT), Independent Cascade (IC), and Susceptible-Infected-Recovered (SIR) across three types of activity networks with different structural characteristics. Our findings reveal significant variations in centrality performance depending on network topology and diffusion dynamics. This methodology integrates structural network analysis with dynamic diffusion modeling to evaluate the effectiveness of influence spread. The experimental results show that out-degree and betweenness centralities are most effective for influence propagation, with the SIR model supporting sustained diffusion. The experimental results reveal that out-degree and betweenness centralities are the most effective measures for influence propagation, with out-degree being particularly impactful for initiating diffusion. The SIR model demonstrated superior efficacy for sustained influence spread, aligning more closely with real-world influence dynamics. Additionally, analyzing influence propagation within WCCs enables more computationally efficient identification of key influencers, without significant loss in accuracy. This work offers actionable insights for influence modeling and provides a practical methodology for selecting centrality measures tailored to specific diffusion scenarios. It explores the influence diffusion across different platforms, enabling researchers to assess and compare user impact by offering a detailed examination of network structures, key node significance and influence diffusion.