A Unified Framework against Topology and Class Imbalance

The Area Under ROC curve (AUC) is widely used as an evaluation metric in various applications. Due to its insensitivity towards class distribution, directly optimizing AUC performs well on the class imbalance problem. However, existing AUC optimization methods are limited to regular data such as text, images, and video. AUC optimization on graph data, which is ubiquitous and important, is seldom studied. Different from regular data, AUC optimization on graphs suffers from not only the class imbalance but also topology imbalance. To solve the complicated imbalance problem, we propose a unified topology-aware AUC optimization (TOPOAUC) framework, which could simultaneously deal with the topology and class imbalance problem in graph learning. We develop a multi-class AUC optimization work to deal with the class imbalance problem. With respect to topology imbalance, we propose a T opology-A ware I mportance L earning mechanism (TAIL), which considers the topology of pairwise nodes and different contributions of topology information to pairwise node neighbors. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method.