Toward Foundation Models for Detecting Abnormal Activities on Graphs

Graph anomaly detection (GAD), which aims to identify rare observations in graphs, has attracted rapidly increasing attention in recent years due to its significance in a wide range of high-impact application domains such as abusive review detection and malicious behavior detection in online shopping applications, web attack detection, and suspicious activity detection in online/offline financial services. A foundation model on GAD refers to a generalist model trained on specific graph data, enabling it to generalize effectively across different domains and tasks. In recent years, such models have attracted increasing attention due to their ability to provide strong zero-shot and few-shot performance without task-specific retraining. By learning domain-invariant and transferable representations across tasks, a GAD foundation model can be readily adapted to new anomaly detection scenarios, making it applicable to a wide range of use cases such as privacy-preserving anomaly detection, transferable cybersecurity and threat detection, and cross-platform anomaly detection in social network.

In this tutorial, we aim to present a comprehensive review of deep learning methods specifically designed for GAD and foundation models for detecting abnormal activities on graphs. Specifically, we will first elaborate on the key concepts and taxonomies in GAD. Then review popular state-of-the-art deep anomaly detection methods from various perspectives of methodology design on graph data, including GNN backbone design, proxy task design, and anomaly measures. Then we will establish the connection between conventional methods and foundation models on GAD, highlighting how recent advancements build upon or differ from conventional approaches. Following this, we will provide a comprehensive overview of existing foundation models that have been proposed for detecting abnormal activities on graphs from cross-domain and cross-task, respectively. We will discuss their underlying principles, design choices, and effectiveness across various settings. The future directions will be finally presented to help researchers gain a deep understanding of this area and promote more high-quality research and real-world applications in the future.