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The paper presents a novel causality-inspired generative model for enhancing the interpretability of Dynamic Graph Neural Networks (DyGNNs) by identifying complex causal relationships in dynamic graphs. It introduces a structural causal model (SCM) to disentangle trivial, static, and dynamic relationships, employing contrastive learning and a dynamic VGAE-based framework for spatial and temporal explanations. Experimental results demonstrate significant improvements in interpretability and predictive perfor

Author

yangkunkuo

Language

EN