24 citations · 63 across the 9 of their papers we have counts for
9 papers · 1 filter
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
Hao Liu, Jiarui Feng, Lecheng Kong +3
Graph Neural Networks (GNNs) have become popular in Graph Representation Learning (GRL). One fundamental application is few-shot node classification. Most existing methods follow t…
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
Zehao Dong, Muhan Zhang, Philip R. O. Payne +5
The expressivity of Graph Neural Networks (GNNs) has been studied broadly in recent years to reveal the design principles for more powerful GNNs. Graph canonization is known as a t…
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
Zehao Dong, Weidong Cao, Muhan Zhang +3
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs amo…
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
Jiarui Feng, Lecheng Kong, Hao Liu +4
Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by th…
Time Associated Meta Learning for Clinical Prediction
Hao Liu, Muhan Zhang, Zehao Dong +5
Rich Electronic Health Records (EHR), have created opportunities to improve clinical processes using machine learning methods. Prediction of the same patient events at different ti…
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design pr…