BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer
  Nodes
Qian Wang Zemin Liu Zhen Zhang Bingsheng He
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.