Past-aware game-theoretic centrality in complex contagion dynamics

TL;DR

This paper introduces a novel past-aware game-theoretic centrality measure that effectively identifies influential nodes in complex contagion networks, improving efficiency and accuracy over traditional methods.

Contribution

It extends standard game-theoretic centrality to incorporate past-aware considerations and develops scalable algorithms for influence maximization in complex contagion processes.

Findings

The new centrality measure outperforms standard methods in influence maximization tasks.

Derived explicit formulas enable scalable computation of influential nodes.

Algorithms show improved efficiency and solution quality in complex contagion models.

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.