PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly
Detection
- URL: http://arxiv.org/abs/2310.11676v3
- Date: Tue, 28 Nov 2023 03:42:30 GMT
- Title: PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly
Detection
- Authors: Junjun Pan, Yixin Liu, Yizhen Zheng, Shirui Pan
- Abstract summary: Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce.
We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD.
Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
- Score: 65.24854366973794
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Node-level graph anomaly detection (GAD) plays a critical role in identifying
anomalous nodes from graph-structured data in various domains such as medicine,
social networks, and e-commerce. However, challenges have arisen due to the
diversity of anomalies and the dearth of labeled data. Existing methodologies -
reconstruction-based and contrastive learning - while effective, often suffer
from efficiency issues, stemming from their complex objectives and elaborate
modules. To improve the efficiency of GAD, we introduce a simple method termed
PREprocessing and Matching (PREM for short). Our approach streamlines GAD,
reducing time and memory consumption while maintaining powerful anomaly
detection capabilities. Comprising two modules - a pre-processing module and an
ego-neighbor matching module - PREM eliminates the necessity for
message-passing propagation during training, and employs a simple contrastive
loss, leading to considerable reductions in training time and memory usage.
Moreover, through rigorous evaluations of five real-world datasets, our method
demonstrated robustness and effectiveness. Notably, when validated on the ACM
dataset, PREM achieved a 5% improvement in AUC, a 9-fold increase in training
speed, and sharply reduce memory usage compared to the most efficient baseline.
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