We propose the Graph Context Encoder (GCE), a simple but efficient approach for graph representation learning based on graph feature masking and reconstruction. GCE models are trained to efficiently reconstruct input graphs similarly to a graph autoencoder where node and edge labels are masked. In particular, our model is also allowed to change graph structures by masking and reconstructing graphs augmented by random pseudo-edges. We show that GCE can be used for novel graph generation, with applications for molecule generation. Used as a pretraining method, we also show that GCE improves baseline performances in supervised classification tasks tested on multiple standard benchmark graph datasets.
@article{arxiv.2106.10124,
title = {Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining},
author = {Oriel Frigo and Rémy Brossard and David Dehaene},
journal= {arXiv preprint arXiv:2106.10124},
year = {2021}
}