Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

• URL: http://arxiv.org/abs/2609.02965v1
• Date: Wed, 02 Sep 2026 07:20:54 GMT
• Title: Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs
• Abstract summary: State-of-the-art deep learning models often struggle to learn especially short-term behavioral interaction signals.<n>We propose a statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space.<n>We evaluate our proposed method on an anomaly detection task across three real-world datasets.
• Score: 0.6967006044904097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic networks are being applied in many domains, from social media to logistics systems, each with their own set of special characteristics. A model employed on this type of data must capture the duality between temporal/structural and feature-based information. Yet state-of-the-art deep learning models often struggle to learn especially short-term behavioral interaction signals, such as sender intensity or interaction inertia, directly from raw event streams. To address this gap, we propose a statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space. We evaluate our proposed method on an anomaly detection task across three real-world datasets (Reddit, Wikipedia, MOOC) and seven models spanning both continuous-time and discrete-time architectures. As a baseline, we apply the same models trained on the original embeddings. Our results show, that augmentation consistently improves detection performance. Beyond performance, the enriched input enables fine-grained post-hoc analysis of behavioral importance, since each statistic occupies a dedicated input dimension. In particular, this work showcases a promising approach for merging classical network analysis with deep learning.

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