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arXiv Machine Learning By Philipp Schlinge, Jean-Luc Schnipper, Martin Atzmueller

Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

Read the original on arXiv Machine Learning →

The paper introduces a statistical feature augmentation technique that encodes behavioral interaction statistics into the input space for dynamic graph anomaly detection. Experiments on Reddit, Wikipedia, and MOOC datasets across seven models—both continuous-time and discrete-time—show that this augmentation consistently improves detection performance compared to models trained on original embeddings. The enriched input also facilitates fine-grained post-hoc analysis of behavioral importance, linking classical network analysis with deep learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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