Exact Deviation Attribution and Amortised Event Explanation for Semi-Supervised Anomaly Detection on Dynamic Graphs
Year: 2026
Type: preprint
Abstract: Semi-supervised detectors for anomaly detection in dynamic graphs are deployed where accountability frameworks require that automated decisions be justified, yet they emit a score and nothing more. For SAD, a continuous-time detector that scores nodes against a time-decayed reference distribution maintained in a memory bank, explanation quality metrics are not low — they are undefined, because no attribution vector exists to evaluate. This paper closes that gap with a framework whose two axes differ in kind. The first supplies event attribution: which past interactions in the temporal neighbourhood produced the decision, learned as an amortised mask that enters the attention logits and is inactive at unity, so detection is preserved to machine precision (∆AUC = 0.0000, ∆AP = 0.0000, ∆P@100 = 0.0000). The second exploits a property unique to this detector: because its anomaly channel is a standardised deviation from an explicit reference, that deviation admits a closed-form decomposition into an instance term and a per-memory-entry baseline term which sum to the total by construction. No sampling, no surrogate, and a completeness residual of 3.8×10−6 on 6,000 real targets confirms it. The decomposition answers a question no other dynamicgraph explainer can pose — whether an alert reflects the node’s own behaviour or a shift in the population baseline — and on MOOC the split is 49.9% against 50.1%. We further conduct a controlled three-seed study of event-attribution strategies, from which three findings follow: amortisation attains characterisation 0.874 against per-instance optimisation’s 0.667 at 453× lower cost, separable at 5.98 standard deviations; a parameter-free gradient baseline ties both trained explainers; and signed fidelity systematically mis-scores anomaly explanations by penalising exculpatory evidence, since random ablation raises the score on 78% of alerts
Source: SSRN Electronic Journal
DOI: 10.2139/ssrn.7339518
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204209839