Causal Influence Maximization in Hypergraph

Influence Maximization (IM) is the task of selecting a fixed number of seed nodes in a given network to maximize dissemination benefits. Although the research for efficient algorithms has been dedicated recently, it is usually neglected to further explore the graph structure and the objective function inherently. With this motivation, we take the first attempt on the hypergraph-based IM with a novel causal objective. We consider the case that each hypergraph node carries specific attributes with Individual Treatment Effect (ITE), namely the change of potential outcomes before/after infections in a causal inference perspective. In many scenarios, the sum of ITEs of the infected is a more reasonable objective for influence spread, whereas it is difficult to achieve via current IM algorithms. In this paper, we introduce a new algorithm called \textbf{CauIM}. We first recover the ITE of each node with observational data and then conduct a weighted greedy algorithm to maximize the sum of ITEs of the infected. Theoretically, we mainly present the generalized lower bound of influence spread beyond the well-known $(1-\frac{1}{e})$ optimal guarantee and provide the robustness analysis. Empirically, in real-world experiments, we demonstrate the effectiveness and robustness of \textbf{CauIM}. It outperforms the previous IM and randomized methods significantly.