  In the context of long-tail classification on graphs, the vast majority of
existing work primarily revolves around the development of model debiasing
strategies, intending to mitigate class imbalances and enhance the overall
performance. Despite the notable success, there is very limited literature that
provides a theoretical tool for characterizing the behaviors of long-tail
classes in graphs and gaining insight into generalization performance in
real-world scenarios. To bridge this gap, we propose a generalization bound for
long-tail classification on graphs by formulating the problem in the fashion of
multi-task learning, i.e., each task corresponds to the prediction of one
particular class. Our theoretical results show that the generalization
performance of long-tail classification is dominated by the overall loss range
and the task complexity. Building upon the theoretical findings, we propose a
novel generic framework HierTail for long-tail classification on graphs. In
particular, we start with a hierarchical task grouping module that allows us to
assign related tasks into hypertasks and thus control the complexity of the
task space; then, we further design a balanced contrastive learning module to
adaptively balance the gradients of both head and tail classes to control the
loss range across all tasks in a unified fashion. Extensive experiments
demonstrate the effectiveness of HierTail in characterizing long-tail classes
on real graphs, which achieves up to 12.9% improvement over the leading
baseline method in accuracy.
