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arXiv · 2106.10124

Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining

Oriel Frigo·Rémy Brossard·David Dehaene
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Abstract

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.

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Oriel Frigo, Rémy Brossard, David Dehaene. 2021-06-18. Graph Context Encoder: Graph Feature Inpainting for Graph Generation and Self-supervised Pretraining. https://arxiv.org/abs/2106.10124

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