  Existing Graph Neural Networks (GNNs) usually assume a balanced situation
where both the class distribution and the node degree distribution are
balanced. However, in real-world situations, we often encounter cases where a
few classes (i.e., head class) dominate other classes (i.e., tail class) as
well as in the node degree perspective, and thus naively applying existing GNNs
eventually fall short of generalizing to the tail cases. Although recent
studies proposed methods to handle long-tail situations on graphs, they only
focus on either the class long-tailedness or the degree long-tailedness. In
this paper, we propose a novel framework for training GNNs, called Long-Tail
Experts for Graphs (LTE4G), which jointly considers the class long-tailedness,
and the degree long-tailedness for node classification. The core idea is to
assign an expert GNN model to each subset of nodes that are split in a balanced
manner considering both the class and degree long-tailedness. After having
trained an expert for each balanced subset, we adopt knowledge distillation to
obtain two class-wise students, i.e., Head class student and Tail class
student, each of which is responsible for classifying nodes in the head classes
and tail classes, respectively. We demonstrate that LTE4G outperforms a wide
range of state-of-the-art methods in node classification evaluated on both
manual and natural imbalanced graphs. The source code of LTE4G can be found at
https://github.com/SukwonYun/LTE4G.
