AutoGraph: Automated Graph Neural Network

TL;DR

AutoGraph introduces an automated method for designing deep Graph Neural Networks using evolutionary algorithms, incorporating skip connections to enhance feature reuse and mitigate vanishing gradients, leading to state-of-the-art results in node classification.

Contribution

It presents a novel automated approach for designing deep GNNs with skip connections and layer growth, reducing manual architecture engineering.

Findings

Achieved state-of-the-art accuracy on Cora, Citeseer, Pubmed, and PPI datasets.

Demonstrated effectiveness of automated GNN design over manually crafted models.

Showed that deeper GNNs with skip connections improve performance.

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.