关键词: 长尾问题, 图变分自编码器, 图潜向量, 生成对抗网络

Abstract: In the real world, many graph datasets suffer from class imbalance issues, typically manifesting at the node, edge, and graph levels. Common oversampling-based methods for addressing graph-level imbalance often lead to model overfitting due to a lack of sample diversity. To address this issue, a graph latent representation distribution learning-based graph oversampling method called GLRD-GAN is proposed. First, a graph latent representation distribution learning method is introduced, utilizing a pre-trained Variational graph auto- encoder(VGAE) and a fully connected neural network to learn the latent representation distribution of minority class graph samples in the low-dimensional space. The latent representation information is randomly sampled on this distribution and fused with the original minority class latent representation, ensuring the diversity of the minority class latent representation. Second, a dual-decoder-based graph generator is designed. The pre-trained inner product decoder and graph convolution decoder make full use of the sampled latent representations to generate the topological structure and node features of graph data, respectively. Finally, a GAN discriminator is employed to detect the authenticity and class of the generated graphs, supervising the effectiveness of the generated samples, thereby achieving the generation of diverse minority class graph samples. Comparative experiments and visualization observations were conducted on five representative long-tail graph datasets. The results show that the proposed graph latent representation distribution learning-based graph oversampling method outperforms other methods by 1%-4% in terms of Acc and F1 scores, and can generate effective minority class graph samples.

Key words: long-tail recognition, variational graph auto-encoder, graph latent representation, generative adversarial network