Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

Graph neural networks (GNNs) have achieved state-of-the-art performance on graph classification tasks, which aim to predict the class labels of entire graphs and have widespread applications. However, existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic complexity. More importantly, real-world graph data are often scattered in different locations. Motivated by these observations, this article presents federated collaborative graph neural networks for few-shot graph classification, termed FCGNN. With its owned graph examples, each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learning model that can generalize to novel categories not seen while training. In each branch, initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggregation layers for few-shot graph classification, which can reduce over-fitting while learning with scarce labeled graph examples. Finally, multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effectively tackle the graph data island issue. Extensive experimental results on few-shot graph classification benchmarks demonstrate the effectiveness and superiority of our proposed framework.