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.
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arxiv.org
Wichtige Erkenntnisse aus
by Yulan Hu,She... um arxiv.org 03-28-2024
https://arxiv.org/pdf/2311.01191.pdfTiefere Fragen