Deep Graph Anomaly Detection: A Survey and New Perspectives

2024-09-16

arXiv.org

AI Summary

Graph anomaly detection (GAD) is critical for security, finance, and other domains, yet existing GNN-based approaches lack systematic organization and a unified analytical framework. To address this, we propose the first comprehensive analysis paradigm grounded in three orthogonal dimensions: GNN backbone design, proxy task construction, and anomaly scoring. We introduce a fine-grained taxonomy comprising 13 categories, decoupling model architecture into backbone networks, pretraining objectives, and anomaly criteria. Integrating GNNs, self-supervised learning, contrastive learning, reconstruction modeling, and multi-scale representation, we establish a reproducible benchmarking suite. Our open-source, continuously updated repository unifies state-of-the-art datasets and algorithms, accompanied by empirical performance comparisons. The study exposes intrinsic limitations of current methods and identifies six key open challenges—providing both theoretical guidance and practical foundations for future GAD research.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Graph anomaly detection (GAD), which aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs), has attracted increasing attention in recent years due to its significance in a wide range of applications. Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD, owing to its strong capability in capturing complex structure and/or node attributes in graph data. Considering the large number of methods proposed for GNN-based GAD, it is of paramount importance to summarize the methodologies and findings in the existing GAD studies, so that we can pinpoint effective model designs for tackling open GAD problems. To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD. Existing GAD surveys are focused on task-specific discussions, making it difficult to understand the technical insights of existing methods and their limitations in addressing some unique challenges in GAD. To fill this gap, we first discuss the problem complexities and their resulting challenges in GAD, and then provide a systematic review of current deep GAD methods from three novel perspectives of methodology, including GNN backbone design, proxy task design for GAD, and graph anomaly measures. To deepen the discussions, we further propose a taxonomy of 13 fine-grained method categories under these three perspectives to provide more in-depth insights into the model designs and their capabilities. To facilitate the experiments and validation, we also summarize a collection of widely-used GAD datasets and empirical comparison. We further discuss multiple open problems to inspire more future high-quality research. A continuously updated repository for datasets, links to the codes of algorithms, and empirical comparison is available at https://github.com/mala-lab/Awesome-Deep-Graph-Anomaly-Detection.

Problem

• Survey deep learning methods for graph anomaly detection
• Analyze challenges in detecting anomalies in graph data
• Propose taxonomy for GNN-based anomaly detection techniques

Innovation

• Uses GNNs for graph anomaly detection
• Reviews methods from three novel perspectives
• Proposes taxonomy with 13 method categories