CATGX: Causal-Aware Temporal Graph Explanation via Scalable Motif Sampling and Adjustment
TL;DR
Experiments demonstrate that CATGX strikes a good balance to generate explanations that are faithful, grounded and confounder-aware, and outperform existing TGNN explainers in efficiency with 83x speed up.
Abstract
Interpreting predictions of temporal graph neural networks (TGNNs) is challenging: structural patterns are entangled with temporal dynamics and node-level activity. Existing methods often overemphasize frequent or recent interactions, producing explanations that conflate structural influence with temporal or behavioral confounding and are not grounded by motif-level evidence. We propose CATGX, a confounder-aware framework for explaining temporal graph predictions through motif-level reasoning. CATGX models temporal interaction mechanisms as motif occurrences, and explicitly treats temporal context and entity activity as observed confounders. By abstracting causal factors into temporal motif, context, and entity codebooks, CATGX applies an adjustment-inspired scoring scheme that compares motif-level influence to isolate structural contributions that persist across comparable conditions. To support fast explanation generation, CATGX integrates graph approximate nearest neighbor (ANN) sampling strategy as an unbiased motif occurrence statistics estimator, which preserves unbiased estimation through importance weighting. The entire pipeline operates in a training-free, model-agnostic manner and achieves bounded, low polynomial-time cost. Experiments demonstrate that CATGX strikes a good balance to generate explanations that are faithful, grounded and confounder-aware, and outperform existing TGNN explainers in efficiency with 83x speed up.