Centrala begrepp
VIGraph introduces a novel approach to address class-imbalanced node classification by generating high-quality nodes directly usable for classification.
Sammanfattning
The content discusses the challenges of class imbalance in graph data and introduces VIGraph as a solution. It delves into the shortcomings of existing methods, particularly in constructing imbalanced graphs. VIGraph relies on Variational Graph Autoencoder (VGAE) and introduces comprehensive training strategies to generate high-quality nodes for minority classes. Extensive experiments demonstrate the superiority and generality of VIGraph.
Statistik
Cora, CiteSeer, PubMed datasets used for experiments.
GraphSmote, GraphMixup, ImGCL, ReNode methods discussed.
VIGraph outperformed other methods in experiments.
Citat
"VIGraph introduces comprehensive training strategies to generate high-quality nodes directly usable for classification."
"VIGraph is more stable than other models under various imbalance ratios, even in extreme imbalance scenarios."