Diversified Multiscale Graph Learning with Graph Self-Correction

Abstract

Though the multiscale graph learning techniques have enabled advanced feature extraction frameworks, the classic ensemble strategy may show inferior performance while encountering the high homogeneity of the learnt representation, which is caused by the nature of existing graph pooling methods. To cope with this issue, we propose a diversified multiscale graph learning model equipped with two core ingredients: a graph self-correction (GSC) mechanism to generate informative embedded graphs, and a diversity boosting regularizer (DBR) to achieve a comprehensive characterization of the input graph. The proposed GSC mechanism compensates the pooled graph with the lost information during the graph pooling process by feeding back the estimated residual graph, which serves as a plug-in component for popular graph pooling methods. Meanwhile, pooling methods enhanced with the GSC procedure encourage the discrepancy of node embeddings, and thus it contributes to the success of ensemble learning strategy. The proposed DBR instead enhances the ensemble diversity at the graph-level embeddings by leveraging the interaction among individual classifiers. Extensive experiments on popular graph classification benchmarks show that the proposed GSC mechanism leads to significant improvements over state-of-the-art graph pooling methods. Moreover, the ensemble multiscale graph learning models achieve superior enhancement by combining both GSC and DBR.

References in corpus (13)

• Semi-Supervised Classification with Graph Convolutional Networks
• Inductive Representation Learning on Large Graphs
• Spectral Networks and Locally Connected Networks on Graphs
• Deep Convolutional Networks on Graph-Structured Data
• Open Graph Benchmark: Datasets for Machine Learning on Graphs
• Gated Graph Sequence Neural Networks
• Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
• TUDataset: A collection of benchmark datasets for learning with graphs
• DeeperGCN: All You Need to Train Deeper GCNs
• Order Matters: Sequence to sequence for sets
• Rethinking pooling in graph neural networks
• MILE: A Multi-Level Framework for Scalable Graph Embedding
• Graph Cross Networks with Vertex Infomax Pooling