Heterogeneous graph neural networks (HGNNs) have exhibited exceptional
efficacy in modeling the complex heterogeneity in heterogeneous information
networks (HINs). The critical advantage of HGNNs is their ability to handle
diverse node and edge types in HINs by extracting and utilizing the abundant
semantic information for effective representation learning. However, as a
widespread phenomenon in many real-world scenarios, the class-imbalance
distribution in HINs creates a performance bottleneck for existing HGNNs. Apart
from the quantity imbalance of nodes, another more crucial and distinctive
challenge in HINs is semantic imbalance. Minority classes in HINs often lack
diverse and sufficient neighbor nodes, resulting in biased and incomplete
semantic information. This semantic imbalance further compounds the difficulty
of accurately classifying minority nodes, leading to the performance
degradation of HGNNs. To tackle the imbalance of minority classes and
supplement their inadequate semantics, we present the first method for the
semantic imbalance problem in imbalanced HINs named Semantic-aware Node
Synthesis (SNS). By assessing the influence on minority classes, SNS adaptively
selects the heterogeneous neighbor nodes and augments the network with
synthetic nodes while preserving the minority semantics. In addition, we
introduce two regularization approaches for HGNNs that constrain the
representation of synthetic nodes from both semantic and class perspectives to
effectively suppress the potential noises from synthetic nodes, facilitating
more expressive embeddings for classification. The comprehensive experimental
study demonstrates that SNS consistently outperforms existing methods by a
large margin in different benchmark datasets.

本研究提出了一种名为 “Semantic-aware Node Synthesis” 的方法，用于处理异质信息网络中少数类别节点的语义不平衡问题，并引入两种正则化方法以抑制合成节点的潜在噪声，从而更有效地进行表示和分类。实验结果表明，该方法在不同的基准数据集中的表现比现有方法都要优异。