{"ID":2888764,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.22589","arxiv_id":"2507.22589","title":"Diffusion Models for Influence Maximization on Temporal Networks: A Guide to Make the Best Choice","abstract":"The increasing prominence of temporal networks in online social platforms and dynamic communication systems has made influence maximization a critical research area. Various diffusion models have been proposed to capture the spread of information, yet selecting the most suitable model for a given scenario remains challenging. This article provides a structured guide to making the best choice among diffusion models for influence maximization on temporal networks. We categorize existing models based on their underlying mechanisms and assess their effectiveness in different network settings. We analyze seed selection strategies, highlighting how the inherent properties of influence spread enable the development of efficient algorithms that can find near-optimal sets of influential nodes. By comparing key advancements, challenges, and practical applications, we offer a comprehensive roadmap for researchers and practitioners to navigate the landscape of temporal influence maximization effectively.","short_abstract":"The increasing prominence of temporal networks in online social platforms and dynamic communication systems has made influence maximization a critical research area. Various diffusion models have been proposed to capture the spread of information, yet selecting the most suitable model for a given scenario remains chall...","url_abs":"https://arxiv.org/abs/2507.22589","url_pdf":"https://arxiv.org/pdf/2507.22589v1","authors":"[\"Aaqib Zahoor\",\"Iqra Altaf Gillani\",\"Janibul Bashir\"]","published":"2025-07-30T11:44:34Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[\"Diffusion Model\"]","has_code":false}