AutoGraph: Automated Graph Neural Network

Abstract

Graphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This causes the low expressive power of the GNNs. To fully utilize the power of the deep neural network, some deep GNNs have been proposed recently. However, the design of deep GNNs requires significant architecture engineering. In this work, we propose a method to automate the deep GNNs design. In our proposed method, we add a new type of skip connection to the GNNs search space to encourage feature reuse and alleviate the vanishing gradient problem. We also allow our evolutionary algorithm to increase the layers of GNNs during the evolution to generate deeper networks. We evaluate our method in the graph node classification task. The experiments show that the GNNs generated by our method can obtain state-of-the-art results in Cora, Citeseer, Pubmed and PPI datasets.

arXiv commentAccepted by ICONIP 2020

References in corpus (4)

• A Comprehensive Survey on Graph Neural Networks
• MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
• Large-Scale Learnable Graph Convolutional Networks
• Dynamic Graph Convolutional Networks

Cited by in corpus (6)

• The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey
• Pooling Architecture Search for Graph Classification
• Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
• Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
• Graph Neural Architecture Search with GPT-4
• AutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networks