Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

2026-09-02

AI Summary

为解决动态图中短期行为信号学习难题，提出统计特征增强方法，提高异常检测性能，并支持细粒度行为分析。

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.

Problem

• anomaly detection
• dynamic graphs
• behavioral interaction signals
• short-term behavior

Innovation

• Statistical Feature Augmentation
• Behavioral Interaction Statistics
• Anomaly Detection
• Dynamic Graphs
• Deep Learning