BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks

Relational data occur in a variety of domains, such as social graphs [25], chemical structures [17], physical systems [25], gene-gene interactions [25], and epidemiological modeling [8]. These data are best represented by graphs that effectively model their relationships, such as chemical bonds in drug molecules that affect toxicity or treatment efficacy [25] or personal interactions in social networks indicating contact [17]. Although graph information represents these datasets more accurately by incorporating node features (i.e., chemical weight for molecules) and node interactions through edges (i.e., chemical bonds) [25], large-scale modeling to learn their patterns can be challenging if the graphs are complex [6, 22]. Embedding methods such as Graphlets[12] and DeepWalk[10] have been developed to address these challenges.