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.

翻译：不平衡节点分类在现实网络广泛存在，其中图神经网络（GNNs）通常高度偏向于多数类别，并在分类少数类别节点时遭受严重的性能下降。近来提出了多种不平衡节点分类方法，这些方法针对少数类别构建合成节点与边，以平衡标签和拓扑分布。然而，它们均基于同质性假设，即相同标签的节点倾向于相连，尽管现实图中广泛存在异质性边。因此，它们统一地从同质与异质邻居中聚合特征，并依赖特征相似性生成合成边，这无法应用于高度异质的不平衡图。为解决此问题，我们提出了一种新颖的GraphSANN模型，用于同质与异质图上的不平衡节点分类。首先，我们提出了一种统一特征混合器，以统一方式通过同质与异质插值生成合成节点。接着，通过随机采样合成节点与现有节点之间的边作为候选边，我们设计了一种自适应子图提取器，以灵活范围自适应提取候选边的上下文子图。最后，我们开发了一种多滤波器子图编码器，构建不同滤波通道，以区分性聚合沿同质与异质边的邻居信息。在八个数据集上的广泛实验表明，我们的模型在同质与异质图上的不平衡节点分类任务中具有优越性。