GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations

Abstract

Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with high cost. It it thus critical to learn graph feature representations in an unsupervised manner in practice. To this end, we propose a novel unsupervised learning of Graph Transformation Equivariant Representations (GraphTER), aiming to capture intrinsic patterns of graph structure under both global and local transformations. Specifically, we allow to sample different groups of nodes from a graph and then transform them node-wise isotropically or anisotropically. Then, we self-train a representation encoder to capture the graph structures by reconstructing these node-wise transformations from the feature representations of the original and transformed graphs. In experiments, we apply the learned GraphTER to graphs of 3D point cloud data, and results on point cloud segmentation/classification show that GraphTER significantly outperforms state-of-the-art unsupervised approaches and pushes greatly closer towards the upper bound set by the fully supervised counterparts. The code is available at: https://github.com/gyshgx868/graph-ter.

References in corpus (18)

• Auto-Encoding Variational Bayes
• Geometric deep learning: going beyond Euclidean data
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Discrete Signal Processing on Graphs
• Graph Neural Networks: A Review of Methods and Applications
• Variational Graph Auto-Encoders
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Adversarially Learned Inference
• Group Equivariant Convolutional Networks
• Learning Representations and Generative Models for 3D Point Clouds
• MolGAN: An implicit generative model for small molecular graphs
• Learning Deep Generative Models of Graphs
• NetGAN: Generating Graphs via Random Walks
• Deep Learning on Graphs: A Survey
• Exploiting Cyclic Symmetry in Convolutional Neural Networks
• Learning Invariant Representations with Local Transformations
• Group Equivariant Capsule Networks
• AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data