Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization

[2502.13571] Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Social and Information Networks arXiv:2502.13571 (cs) [Submitted on 19 Feb 2025 ( v1 ), last revised 14 Apr 2026 (this version, v2)] Title: Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization Authors: Hongliang Qiao , Shanshan Feng , Min Zhou , Xutao Li , Yunming Ye , Fan Li , Shuo Shang , Yew-Soon Ong View a PDF of the paper titled Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization, by Hongliang Qiao and 7 other authors View PDF HTML (experimental) Abstract: The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diffusion models with known parameters, limiting their generalization to real-world scenarios. In contrast, graph representation learning-based methods have gained wide attention for overcoming this limitation by learning user representations to capture influence characteristics. However, existing studies are built on Euclidean space, which fails to effectively capture the latent hierarchical features of social influence distribution. As a result, users' influence spread cannot be effectively measured through the learned representations. To alleviate these limitations, we propose HIM, a novel diffusion model agnostic method that leverages hyperbolic representation learning to estimate users' potential influence spread from social propagation data. HIM consists of two key components. First, a hyperbolic influence representation module encodes influence spread patterns from network structure and historical influence activations into expressive hyperbolic user representations. Hence, the influence magnitude of users can be reflected through the geometric properties of hyperbolic space, where highly influential users tend to cluster near the space origin. Second, a novel adaptive seed selection module is developed to flexibly and effectively select seed users using the positional information of learned user representations. Extensive experiments on five network datasets demonstrate the superior effectiveness and efficiency of our method for the IM problem with unknown diffusion model parameters, highlighting its potential for large-scale real-world social networks. Comments: 14 pages, 10 figures Subjects: Social and Information Networks (cs.SI) ; Machine Learning (cs.LG) Cite as: arXiv:2502.13571 [cs.SI] (or arXiv:2502.13571v2 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2502.13571 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/TKDE.2026.3683562 Focus to learn more DOI(s) linking to related resources Submission history From: Hongliang Qiao [ view email ] [v1] Wed, 19 Feb 2025 09:24:28 UTC (1,474 KB) [v2] Tue, 14 Apr 2026 03:18:34 UTC (3,087 KB) Full-text links: Access Paper: View a PDF of the paper titled Influence Strength Estimation in Hyperbolic Space for Social Influence Maximization, by Hongliang Qiao and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SI < prev | next > new | recent | 2025-02 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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