Zero-Degradation Post-Hoc Explanations with Identity Pass-Through for Recurrent Graph Anomaly Detection
Year: 2026
Type: preprint
Abstract: Anomaly detection in dynamic graphs underpins high-stakes applications in financial fraud analysis, intrusion detection, and online platform integrity, where accountability frameworks increasingly require that automated decisions be accompanied by human-interpretable justifications. StrGNN, the strongest performer in recent unified benchmarks of the field, delivers this detection quality while producing no explanation whatsoever: when an edge is flagged, the analyst receives a score and nothing more. Explanation quality metrics are not low for StrGNN — they are undefined, because no attribution vector exists to evaluate. 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, two complementary attributions: a structural attribution over the enclosing-subgraph evidence identifying which contextual interactions drove the decision, and a temporal attribution over the observation window identifying which historical snapshot carried the signal. Because both attributions are realised as multiplicative masks that are identically one in the unexplained pass, the layer is an exact pass-through: detection is preserved to machine precision, which we verify rather than assert (∆AUC = 0.0000, ∆AP = 0.0000, ∆P@100 = 0.0000). Attribution costs 0.66 ms per flagged edge, making explanation of an entire alarm list feasible rather than a hand-selected handful. We further 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 attribution stability in the study (0.913) at 268× 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 own ablated control, while per-instance optimisation — the most expensive strategy evaluated — falls below that floor. Code, protocol, and per-seed measurements are released.
Source: SSRN Electronic Journal
DOI: 10.2139/ssrn.7339579
Institutions: École Nationale Supérieure d'Informatique, Lyon 1 Université
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204201896