Unified Graph Embedding-Based Anomalous Edge Detection

Abstract

Detecting anomalous edges in graph-structured data plays an important role in many fields such as finance, social network, and network security. Recently, graph embedding based anomaly detection methods show promising results. These methods typically encode graph structure information into vector representation and apply general anomaly detection methods. However, since the parameters in these two parts are learned separately with different objectives, the learned representation may contain some information irrelevant to the task. It would be ideal if we can combine representation learning and anomaly detection into one objective function to force the model to focus on learning task relevant patterns. In this paper, we propose a novel end-to-end neural network architecture that can accurately estimate the probability distribution of edges in the graph based on its local structure. An edge has a high chance to be considered an anomaly if the probability of its existence is low. Extensive experiments on several public datasets at different scales show that the accuracy and scalability of our method outperform other methods by a large margin.