GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node
  Classification
Wen-Zhi Li Chang-Dong Wang Hui Xiong Jian-Huang Lai
Class imbalance is the phenomenon that some classes have much fewer instances
than others, which is ubiquitous in real-world graph-structured scenarios.
Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would
under-represent minor class samples. We investigate this phenomenon and
discover that the subspaces of minor classes being squeezed by those of the
major ones in the latent space is the main cause of this failure. We are
naturally inspired to enlarge the decision boundaries of minor classes and
propose a general framework GraphSHA by Synthesizing HArder minor samples.
Furthermore, to avoid the enlarged minor boundary violating the subspaces of
neighbor classes, we also propose a module called SemiMixup to transmit
enlarged boundary information to the interior of the minor classes while
blocking information propagation from minor classes to neighbor classes.
Empirically, GraphSHA shows its effectiveness in enlarging the decision
boundaries of minor classes, as it outperforms various baseline methods in
class-imbalanced node classification with different GNN backbone encoders over
seven public benchmark datasets. Code is avilable at
https://github.com/wenzhilics/GraphSHA.