Past-aware game-theoretic centrality in complex contagion dynamics
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917115257159680 |
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| author | Zigliotto, Francesco |
| author_facet | Zigliotto, Francesco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Past-aware game-theoretic centrality in complex contagion dynamics Zigliotto, Francesco Social and Information Networks 91D30 (Primary) 05C57, 05C85 (Secondary) 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. |
| title | Past-aware game-theoretic centrality in complex contagion dynamics |
| topic | Social and Information Networks 91D30 (Primary) 05C57, 05C85 (Secondary) |
| url | https://arxiv.org/abs/2511.07157 |