  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.
