CausalSKyHop: Knowledge-Aware Causal Explanation of Dynamic GNNs via Higher-Order Semantic Reasoning

Dynamic Graph Neural Networks (DyGNNs) are widely used to model web-scale semantic-rich graph data (e.g., social networks, knowledge graphs), but their inability to explain predictions grounded in structured knowledge remains a challenge, especially when predictions rely on complex higher-order substructures. We propose CausalSKyHop, a semantic-and knowledge-aware framework that explains DyGNNs by uncovering causal higher-order patterns in evolving knowledge structures. To model the semantic fabric of the graph, CausalSKyHop incorporates a Higher-Order Structural Causal Model to capture multi-node knowledge dependencies, and uses contrastive learning to isolate semantically-meaningful causal relationships from spurious ones. A dynamic correlation module further separates persistent knowledge from evolving semantic contexts. Through knowledge-infused, structure-aware variational graph autoencoders, our method produces interpretable causal subgraphs that capture the dynamic flow of knowledge and semantics. Experimental evaluations on multiple web and knowledge-rich graph benchmarks demonstrate that CausalSKyHop consistently outperforms state-of-the-art explainable DyGNNs, achieving notable improvements in both explanation fidelity and downstream prediction accuracy. A detailed case study further illustrates how our method uncovers stable, semantically coherent causal pathways-in contrast to the fragmented explanations of baseline methods-providing intuitive evidence for its superior interpretability. This work establishes the critical role of explicit semantic and knowledge integration through higher-order causal reasoning for building transparent and trustworthy DyGNNs on the web. CCS Concepts • Theory of computation → Semantics and reasoning; • Computing methodologies → Causal reasoning and diagnostics.