2 research outputs found
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer Nodes
Class imbalance in graph-structured data, where minor classes are
significantly underrepresented, poses a critical challenge for Graph Neural
Networks (GNNs). To address this challenge, existing studies generally generate
new minority nodes and edges connecting new nodes to the original graph to make
classes balanced. However, they do not solve the problem that majority classes
still propagate information to minority nodes by edges in the original graph
which introduces bias towards majority classes. To address this, we introduce
BuffGraph, which inserts buffer nodes into the graph, modulating the impact of
majority classes to improve minor class representation. Our extensive
experiments across diverse real-world datasets empirically demonstrate that
BuffGraph outperforms existing baseline methods in class-imbalanced node
classification in both natural settings and imbalanced settings. Code is
available at https://anonymous.4open.science/r/BuffGraph-730A
Semantic-aware Node Synthesis for Imbalanced Heterogeneous Information Networks
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