  Learning unbiased node representations under class-imbalanced graph data is
challenging due to interactions between adjacent nodes. Existing studies have
in common that they compensate the minor class nodes `as a group' according to
their overall quantity (ignoring node connections in graph), which inevitably
increase the false positive cases for major nodes. We hypothesize that the
increase in these false positive cases is highly affected by the label
distribution around each node and confirm it experimentally. In addition, in
order to handle this issue, we propose Topology-Aware Margin (TAM) to reflect
local topology on the learning objective. Our method compares the connectivity
pattern of each node with the class-averaged counter-part and adaptively
adjusts the margin accordingly based on that. Our method consistently exhibits
superiority over the baselines on various node classification benchmark
datasets with representative GNN architectures.
