Search Results for author:
Found 2 papers, 0 papers with code
Contrastive Disentangled Learning on Graph for Node Classification
Inspired by the success of contrastive learning, we propose a novel framework for contrastive disentangled learning on graphs, employing a disentangled graph encoder and two carefully crafted self-supervision signals.
Variational Disentangled Graph Auto-Encoders for Link Prediction
The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors, which contributes to improving the performance of link prediction.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
