面向动态图异常检测的统计特征增强方法
Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs
- German Research Centre for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对动态图异常检测中深度学习模型难学习短期交互信号的问题，提出统计特征增强方法，在三个真实数据集和七种模型上验证其可提升检测性能，支持细粒度行为分析，展示了经典网络分析与深度学习结合的可行路径。
AI中文摘要：
动态网络已应用于从社交媒体到物流系统等多个领域，各领域具有独特特性，针对此类数据的模型需捕捉时间/结构信息与基于特征的信息之间的二元性。然而，最先进的深度学习模型往往难以直接从原始事件流中学习到短期行为交互信号，如发送者强度或交互惯性。为解决这一问题，我们提出一种统计特征增强方法，将行为交互统计信息显式编码到输入特征空间中。我们在三个真实世界数据集（Reddit、Wikipedia、MOOC）及涵盖连续时间和离散时间架构的七种模型上，针对异常检测任务评估所提方法；作为基线，我们对在原始嵌入上训练的相同模型进行评估。结果表明，特征增强可持续提升检测性能。除性能外，丰富的输入还支持对行为重要性的细粒度事后分析，因为每个统计量占据一个专用输入维度。本研究展示了将经典网络分析与深度学习相结合的可行方法。
英文摘要：
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.
