Graph Classification by Mixture of Diverse Experts

Abstract

Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world datasets. Despite their success, most of current GNN models largely overlook the important setting of imbalanced class distribution, which typically results in prediction bias towards majority classes. To alleviate the prediction bias, we propose to leverage semantic structure of dataset based on the distribution of node embedding. Specifically, we present GraphDIVE, a general framework leveraging mixture of diverse experts (i.e., graph classifiers) for imbalanced graph classification. With a divide-and-conquer principle, GraphDIVE employs a gating network to partition an imbalanced graph dataset into several subsets. Then each expert network is trained based on its corresponding subset. Experiments on real-world imbalanced graph datasets demonstrate the effectiveness of GraphDIVE.

References in corpus (13)

• Inductive Representation Learning on Large Graphs
• Geometric deep learning: going beyond Euclidean data
• Fast Graph Representation Learning with PyTorch Geometric
• Open Graph Benchmark: Datasets for Machine Learning on Graphs
• Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
• Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
• Learning Factored Representations in a Deep Mixture of Experts
• Remix: Rebalanced Mixup
• Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
• Inductive Matrix Completion Based on Graph Neural Networks
• Equalization Loss for Long-Tailed Object Recognition
• Optimal Transport Graph Neural Networks