IBCS: Learning Information Bottleneck-Constrained Denoised Causal Subgraph for Graph Classification

The significant success of graph learning has provoked a meaningful but challenging task of extracting the precise causal subgraphs that can interpret and improve the predictions. Unfortunately, current works merely center on partially eliminating either the spurious or the noisy parts, while overlook the fact that in more practical and general situations, both the spurious and noisy subgraph coexist with the causal one. This brings great challenges and makes previous methods fail to extract the true causal substructure. Unlike existing studies, in this paper, we propose a more reasonable problem formulation that hypothesizes the graph is a mixture of causal, spurious, and noisy subgraphs. With this regard, an Information Bottleneck-constrained denoised Causal Subgraph (IBCS) learning model is developed, which is capable of simultaneously excluding the spurious and noisy parts. Specifically, for the spurious correlation, we design a novel causal learning objective, in which beyond minimizing the empirical risks of causal and spurious subgraph classification, the intervention is further conducted on spurious features to cut off its correlation with the causal part. On this basis, we further impose the information bottleneck constraint to filter out label-irrelevant noise information. Theoretically, we prove that the causal subgraph extracted by our IBCS can approximate the ground-truth. Empirically, extensive evaluations on nine benchmark datasets demonstrate our superiority over state-of-the-art baselines.