Deep graph learning has gained grand popularity over the past years due to
its versatility and success in representing graph data across a wide range of
domains. However, the pervasive issue of imbalanced graph data distributions,
where certain parts exhibit disproportionally abundant data while others remain
sparse, undermines the efficacy of conventional graph learning algorithms,
leading to biased outcomes. To address this challenge, Imbalanced Graph
Learning (IGL) has garnered substantial attention, enabling more balanced data
distributions and better task performance. Despite the proliferation of IGL
algorithms, the absence of consistent experimental protocols and fair
performance comparisons pose a significant barrier to comprehending
advancements in this field. To bridge this gap, we introduce IGL-Bench, a
foundational comprehensive benchmark for imbalanced graph learning, embarking
on 16 diverse graph datasets and 24 distinct IGL algorithms with uniform data
processing and splitting strategies. Specifically, IGL-Bench systematically
investigates state-of-the-art IGL algorithms in terms of effectiveness,
robustness, and efficiency on node-level and graph-level tasks, with the scope
of class-imbalance and topology-imbalance. Extensive experiments demonstrate
the potential benefits of IGL algorithms on various imbalanced conditions,
offering insights and opportunities in the IGL field. Further, we have
developed an open-sourced and unified package to facilitate reproducible
evaluation and inspire further innovative research, which is available at
this https URL

为了解决不平衡图数据在传统图学习算法中所导致的偏颇结果问题，本研究引入了 IGL-Bench，它是一种用于不平衡图学习的全面基准测试工具，研究了 16 个不同的图数据集和 24 种不同的 IGL 算法，通过一致的数据处理和划分策略，系统地评估了 IGL 算法的效力、鲁棒性和效率，并展示了这些算法在不同不平衡条件下的潜在益处，为 IGL 领域的进一步研究提供了机会和启示。