Amortised Post-Hoc Explanation with Exact Preservation for Dynamic Graph Anomaly Detectors

2026-08-16

AI Summary

This study addresses the lack of interpretability and attribution capabilities in StrGNN for dynamic graph anomaly detection by proposing X-StrGNN, a novel explanation layer. Leveraging amortized parameterization and multiplicative masking strategies, this method provides dual post-hoc structural and temporal attributions while strictly preserving the original detection performance. As the first explanation framework achieving zero performance degradation, X-StrGNN balances high stability with low computational cost. Experimental results demonstrate that the model maintains identical detection metrics while requiring only 0.66 ms per edge for explanation generation and achieving a stability score of 0.913. Notably, the computational overhead is reduced by 268 times compared to instance-wise optimization, thereby realizing efficient and stable interpretability enhancement for dynamic graph anomaly detection without compromising accuracy.

Abstract

Anomaly detection in dynamic graphs underpins financial fraud analysis, intrusion detection, and platform integrity, where automated decisions require human-interpretable justifications. StrGNN, the strongest performer in recent benchmarks, produces no explanation: when an edge is flagged, the analyst receives only a score. Explanation metrics are undefined for StrGNN because no attribution vector exists. This paper closes that gap. We present X-StrGNN, a post-hoc explanation layer that wraps a trained, frozen StrGNN and emits, for every flagged edge, dual attributions: a structural attribution identifying which contextual interactions in the enclosing subgraph drove the decision, and a temporal attribution identifying which historical snapshot carried the signal. Both attributions are multiplicative masks identically one in the unexplained pass, so the layer is an exact pass-through: detection is preserved to machine precision, verified rather than asserted (Delta AUC = 0.0000, Delta AP = 0.0000, Delta P@100 = 0.0000). Attribution costs 0.66 ms per edge, making explanation of an entire alarm list feasible. We conduct the first controlled design study of attribution strategies for this architecture, comparing gradient attribution, per-instance mask optimisation, and amortised parameterisation under one protocol, one budget, and three seeds. X-StrGNN attains the highest stability (0.913) at 268x lower cost than per-instance optimisation, and its temporal attribution (1.601 against a measured random floor of 0.973) is separably better than its ablated control, while per-instance optimisation - the most expensive strategy - falls below that floor. Code, protocol, and per-seed measurements are released.

Problem

• Dynamic Graph Anomaly Detection
• Post-Hoc Explanation
• StrGNN
• Interpretability

Innovation

• Amortised Post-Hoc Explanation
• Exact Preservation
• Dynamic Graph Anomaly Detection
• Dual Attributions
• Multiplicative Masks