A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions

TL;DR

This survey reviews the challenges and solutions for imbalanced learning in graph analytics, categorizing problems and techniques, and outlining future research directions to improve graph-based machine learning tasks.

Contribution

It provides a comprehensive taxonomy of imbalanced learning problems and techniques on graphs, and discusses future research directions in this emerging field.

Findings

Taxonomy of imbalance problems and tasks in graph learning

Classification of techniques for addressing data imbalance on graphs

Identification of promising future research directions

Abstract

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning various tasks including node classification and link prediction. However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce, thereby leading to biased learning outcomes. This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes. In this survey, we embark on a comprehensive review of the literature on imbalanced learning on graphs. We begin by providing a definitive understanding of the concept and related terminologies, establishing a strong foundational understanding for readers. Following this, we propose two comprehensive taxonomies: (1) the problem taxonomy, which describes the forms of imbalance we consider, the associated tasks, and potential solutions; (2) the technique taxonomy, which details key strategies for addressing these imbalances, and aids readers in their method selection process. Finally, we suggest prospective future directions for both problems and techniques within the sphere of imbalanced learning on graphs, fostering further innovation in this critical area.