  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.
