Class-Imbalanced Learning on Graphs: A Survey

In recent years, graph representation learning techniques have proven effective in discovering meaningful vector representations of nodes, edges, or entire graphs, resulting in successful applications across a wide range of downstream tasks [29, 52, 68]. However, graph data often presents a significant challenge in the form of class imbalance, where one class's instances significantly outnumber those of other classes. This imbalance can lead to suboptimal performance when applying machine learning techniques to graph data. Class-imbalanced learning on graphs (CILG) is an emerging research area addressing class imbalance in graph data, where traditional methods for non-graph data might be unsuitable or ineffective for several reasons. Firstly, graph data's unique, irregular, non-Euclidean structure complicates traditional class-imbalance techniques designed for Euclidean data [78]. Secondly, graph data often holds rich relational information, necessitating specialized techniques for preservation and leverage during the learning process [51]. Lastly, node dependencies and interactions in a graph make class re-balancing complex, as naïve oversampling or undersampling may disrupt the graph's structure and thus lead to poor performance [35].