BOURNE: Bootstrapped Self-Supervised Learning Framework for Unified Graph Anomaly Detection

Jie Liu, Mengting He, Xuequn Shang, Jieming Shi, Bin Cui, Hongzhi Yin

科研成果: 书/报告/会议事项章节会议稿件同行评审

8 引用 (Scopus)

摘要

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies.

源语言英语
主期刊名Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024
出版商IEEE Computer Society
2820-2833
页数14
ISBN(电子版)9798350317152
DOI
出版状态已出版 - 2024
活动40th IEEE International Conference on Data Engineering, ICDE 2024 - Utrecht, 荷兰
期限: 13 5月 202417 5月 2024

出版系列

姓名Proceedings - International Conference on Data Engineering
ISSN(印刷版)1084-4627
ISSN(电子版)2375-0286

会议

会议40th IEEE International Conference on Data Engineering, ICDE 2024
国家/地区荷兰
Utrecht
时期13/05/2417/05/24

指纹

探究 'BOURNE: Bootstrapped Self-Supervised Learning Framework for Unified Graph Anomaly Detection' 的科研主题。它们共同构成独一无二的指纹。

引用此