Influence Persistence Maximization in Temporal Social Networks

In this paper, we investigate a novelInfluencePersistenceMaximization (InfPM) problem in temporal social networks. Given a temporal graph, InfPM aims to identify a fixed seed node set$S$that maximizes the total duration of persistent influence across consecutive snapshots. After proving that InfPM is NP-hard, monotonic, and non-submodular, we develop two efficient solutions: (1) RevG, a reverse greedy algorithm that iteratively removes low-contribution nodes, and (2) LRep, a replacement-based method that progressively improves the quality of seed node set. To accelerate influence computation in RevG and LRep, we propose a new influence computation method integrating snapshot compression, probability-aware sampling, and a specialized influence estimator offering unbiased estimation. Additionally, we explore a practical variant of InfPM, termed Win-InfPM, which relaxes the requirement of consecutive snapshots by introducing a flexible time window model. Extensive experiments on seven real-world networks demonstrate that (1) RevG and LRep effectively identify high-quality seed nodes, achieving up to 100% improvement in total influence persistence over the baselines; and (2) the proposed influence computation method improves the efficiency of RevG and LRep by up to 400%, while maintaining comparable influence persistence.