Title: Escaping the Homophily Trap: A Threshold-free Graph Outlier Detection Framework via Clustering-guided Edge Reweighting
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Outlier Detection, Graph Neural Networks, Clustering

Abstract: Graph outlier detection is a critical task for identifying rare, deviant patterns in graph-structured data. 
However, prevalent methods based on graph convolution are fundamentally challenged by the ''Homophily Trap'': the aggregation of features from neighboring nodes inadvertently contaminates the representations of normal nodes near anomalies, blurring their distinctions. 
To overcome this limitation, we propose a Clustering-guided Edge Reweighting framework for Graph Outlier Detection (CER-GOD), which jointly optimizes a self-discriminative masking spoiler with an adaptive clustering-based outlier detector. 
The masking spoiler learns to selectively weaken the influence of heterogeneous neighbors, preserving the discriminative power of node embeddings. 
This process is guided by the clustering detector, which generates pseudo-labels in an unsupervised manner, thereby eliminating the need for predefined anomaly thresholds. 
To ensure robust optimization and prevent class collapse—a failure mode exacerbated by the homophily trap—we introduce a diversity loss that stabilizes the clustering process. 
Our end-to-end framework demonstrates superior performance on multiple benchmark datasets, establishing a new state-of-the-art by effectively dismantling the homophily trap.