Causal-Inspired Influence Maximization in Hypergraphs Under Temporal Constraints

Xinyan Su, Jiyan Qiu, Zhiheng Zhang, Jun Li

Abstract: Influence Maximization is a significant problem aimed to find a set of seed nodes to maximize the spread of given events in social networks. Previous studies are contributing to the efficiency and online dynamics of basic IM on classical graph structure. However, they lack an adequate consideration of individual and group behavior on propagation probability. This can be attributed to inadequate attention given to node Individual Treatment Effects (ITE), which significantly impacts the probability of propagation by dividing the sensitive attributes of nodes. Additionally, current research lacks exploration of temporal constraints in influence spreading process under higher order interference on hypergraphs. To fill these two gaps, we introduce two sets of basic assumptions about the impact of ITE on the propagation process and develop a new diffusion model called the Latency Aware Contact Process on Causal Independent Cascading (LT-CPCIC) under time constraints on hypergraphs. We further design Causal-Inspired Cost-Effective Balanced Selection algorithm (CICEB) for the proposed models. CICEB first recovers node ITE from observational data and applies three types of debiasing strategies, namely DebiasFormer, DebiasCur and DebiasInteg, to weaken the correlation between the propagation effects of different pre- and post-nodes. Finally, we compare CICEB with traditional methods on two real-world datasets and show that it achieves superior effectiveness and robustness.