  Imbalanced node classification widely exists in real-world networks where
graph neural networks (GNNs) are usually highly inclined to majority classes
and suffer from severe performance degradation on classifying minority class
nodes. Various imbalanced node classification methods have been proposed
recently which construct synthetic nodes and edges w.r.t. minority classes to
balance the label and topology distribution. However, they are all based on the
homophilic assumption that nodes of the same label tend to connect despite the
wide existence of heterophilic edges in real-world graphs. Thus, they uniformly
aggregate features from both homophilic and heterophilic neighbors and rely on
feature similarity to generate synthetic edges, which cannot be applied to
imbalanced graphs in high heterophily. To address this problem, we propose a
novel GraphSANN for imbalanced node classification on both homophilic and
heterophilic graphs. Firstly, we propose a unified feature mixer to generate
synthetic nodes with both homophilic and heterophilic interpolation in a
unified way. Next, by randomly sampling edges between synthetic nodes and
existing nodes as candidate edges, we design an adaptive subgraph extractor to
adaptively extract the contextual subgraphs of candidate edges with flexible
ranges. Finally, we develop a multi-filter subgraph encoder that constructs
different filter channels to discriminatively aggregate neighbor's information
along the homophilic and heterophilic edges. Extensive experiments on eight
datasets demonstrate the superiority of our model for imbalanced node
classification on both homophilic and heterophilic graphs.
