Dual Spatial-Temporal Shapley Attribution for Explainable Anomaly Detection in Dynamic Social Graphs

Anomaly detection in dynamic social graphs is a critical challenge across high-stakes settings including online trust and rating communities, coordinated inauthentic behaviour, social platform moderation and fraud analysis, where accountability frameworks demand that automated decisions be accompanied by human-interpretable justifications. State-of-theart Transformer-based detectors such as TADDY achieve strong detection performance but produce no explanation whatsoever for their decisions: when an edge is flagged as anomalous, the analyst receives a score and nothing more. This paper closes that gap. We present a post-hoc explainability framework that wraps a trained, frozen TADDY model and produces, for each flagged edge, two complementary attribution vectors: a spatial attribution identifying which neighbour nodes drove the anomaly, and a temporal attribution identifying which historical snapshots carried the strongest signal. This dual decomposition answers two distinct questions that no prior method addresses simultaneously. The framework adapts the GraphSVX Shapley surrogate to TADDY’s windowed token architecture through a graph-aware embedding-space perturbation strategy that avoids the pathological artefacts of naive index-space masking. We evaluate on three dynamic social graph benchmarks. Prior to this work, all explanation quality metrics are undefined for TADDY. After applying our framework, Fidelity reaches 0.9562, Sufficiency 0.9658, and positive Comprehensiveness is confirmed across all datasets, at zero cost to detection performance (∆AUC = 0.0000). An expanded evaluation covering 2,471 edges across all true positives, false positives and low-confidence detections confirms robustness at scale.