{"ID":2843998,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07157","arxiv_id":"2511.07157","title":"Past-aware game-theoretic centrality in complex contagion dynamics","abstract":"In this paper, we introduce past-aware game-theoretic centrality, a class of centrality measures that captures the collaborative contribution of nodes in a network, accounting for both uncertain and certain collaborators. A general framework for computing standard game-theoretic centrality is extended to the past-aware case. As an application, we develop a new heuristic for different versions of the influence maximization problem in complex contagion, which models processes requiring reinforcement from multiple neighbors to spread. A computationally efficient explicit formula for the corresponding past-aware centrality score is derived, leading to scalable algorithms for identifying the most influential nodes, which in most cases outperform the standard greedy approach in both efficiency and solution quality.","short_abstract":"In this paper, we introduce past-aware game-theoretic centrality, a class of centrality measures that captures the collaborative contribution of nodes in a network, accounting for both uncertain and certain collaborators. A general framework for computing standard game-theoretic centrality is extended to the past-aware...","url_abs":"https://arxiv.org/abs/2511.07157","url_pdf":"https://arxiv.org/pdf/2511.07157v2","authors":"[\"Francesco Zigliotto\"]","published":"2025-11-10T14:48:40Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[]","has_code":false}