MOTIF-Driven Contrastive Learning of Graph Representations

No. 18: AAAI-21 Student Papers and Demonstrations

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 35

AAAI Undergraduate Consortium

We propose a MOTIF-driven contrastive framework to pretrain a graph neural network in a self-supervised manner so that it can automatically mine motifs from large graph datasets. Our framework achieves state-of-the-art results on various graph-level downstream tasks with few labels, like molecular property prediction.

10.1609/aaai.v35i18.17986