Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection
The paper introduces X-AddGraph, the first post-hoc explainability framework for AddGraph, a GCN+GRU model for edge-level anomaly detection in dynamic graphs. Its Dual Spatial-Temporal Attribution (DSTA) mechanism aligns spatial, short-term temporal, and long-term temporal explanations with AddGraph's architecture while keeping the detector frozen, achieving zero performance loss (Delta AUC=0). On UCI Message, the baseline reaches 0.8705 average per-snapshot AUC and all scores are reproduced; long-term attribution surfaces historical snapshots with significantly more counterfactual signal than random (0.127 vs 0.074).
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[Submitted on 12 Aug 2026]
Title:Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection
View a PDF of the paper titled Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection, by Iyad Assaad Nekka and 3 other authors
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Abstract:Deep learning detectors for anomalies in dynamic graphs have reached strong accuracy, yet they remain opaque: when an edge is flagged, the analyst receives a score but no reason. This opacity is untenable in the cooperative, regulated information systems where such detectors are deployed, where automated decisions must be auditable and trustworthy. We address this gap for AddGraph, the foundational GCN+GRU framework for edge-level anomaly detection in dynamic graphs, which to our knowledge has never been equipped with any form of explainability. We present a strictly post-hoc explainability framework, X-AddGraph, built on a Dual Spatial-Temporal Attribution (DSTA) mechanism whose three components are each aligned with one of AddGraph's architectural modules: a gradient-based relevance attribution over the current adjacency structure (spatial), a direct reading of the contextual attention weights already computed during inference (short-term temporal, at zero additional cost), and a gradient rollback through the recurrent hidden states (long-term temporal). Because the detector is frozen, detection performance is preserved exactly (Delta AUC = 0, verified empirically to ten decimal places). On the UCI Message benchmark, our trained AddGraph baseline reaches an average per-snapshot AUC of 0.8705, exceeding the originally published result; X-AddGraph reproduces every score identically while adding explanations where none existed. Evaluated across four edge populations - confident true positives, low-confidence true positives, false positives, and random samples - the long-term attribution identifies historical snapshots carrying significantly more counterfactual signal than random selection (0.127 vs. 0.074), a capability that no spatially-blind explainer can provide. We release our implementation for full reproducibility.
Comments: Under Review : The International Conference on Cooperative Information Systems (CoopIS)
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.12441 [cs.LG]
(or arXiv:2608.12441v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.12441
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Iyad Assaad Nekka [view email] [v1] Wed, 12 Aug 2026 15:58:27 UTC (13 KB)
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