ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks

Qu，Liang1; Zhu，Huaisheng1; Zheng，Ruiqi1; Shi，Yuhui2; Yin，Hongzhi1

10.1145/3447548.3467334

2021-08-14

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

1390-1398

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) have shown promising performance on many network analysis tasks. However, most existing GNNs have almost exclusively focused on the balanced networks, and would get unappealing performance on the imbalanced networks. To bridge this gap, in this paper, we present a generative adversarial graph network model, called ImGAGN to address the imbalanced classification problem on graphs. It introduces a novel generator for graph structure data, named GraphGenerator, which can simulate both the minority class nodes' attribute distribution and network topological structure distribution by generating a set of synthetic minority nodes such that the number of nodes in different classes can be balanced. Then a graph convolutional network (GCN) discriminator is trained to discriminate between real nodes and fake (i.e., generated) nodes, and also between minority nodes and majority nodes on the synthetic balanced network. To validate the effectiveness of the proposed method, extensive experiments are conducted on four real-world imbalanced network datasets. Experimental results demonstrate that the proposed method ImGAGN outperforms state-of-the-art algorithms for semi-supervised imbalanced node classification task.

generative adversarial networks graph neural networks imbalanced networks node classification

第一

英语

EI

20213810905661

Convolutional neural networks ; Data mining ; Graph theory

Data Processing and Image Processing:723.2 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85114905907

Scopus

1.Southern University of Science and Technology,Shenzhen,China
2.The University of Queensland,Brisbane,Australia

Qu，Liang,Zhu，Huaisheng,Zheng，Ruiqi,et al. ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks[C],2021:1390-1398.