Yang Shen

Bringing Your Own View

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and …

Graph Domain Adaptation via Theory-Grounded Spectral Regularization.

2023 · PubMed

, respectively. In a nut-shell, our study paves the way toward explicitly constructing and training GNNs that can capture more transferable representations across graph domains. Codes are released at https://github.com/Shen-Lab/GDA-SpecReg.

Graph Contrastive Learning with Augmentations

2020 · arXiv (Cornell University)

Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training …