280 citations · 429 across the 11 of their papers we have counts for
11 papers
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…
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Zehao Dong, Muhan Zhang, Fuhai Li +1
Optimization of directed acyclic graph (DAG) structures has many applications, such as neural architecture search (NAS) and probabilistic graphical model learning. Encoding DAGs in…
Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
Changlin Wan, Muhan Zhang, Wei Hao +3
Hypergraph offers a framework to depict the multilateral relationships in real-world complex data. Predicting higher-order relationships, i.e hyperedge, becomes a fundamental probl…
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs
Muhan Zhang, Shali Jiang, Zhicheng Cui +2
Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as man…
Link Prediction Based on Graph Neural Networks
Muhan Zhang, Yixin Chen
Link prediction is a key problem for network-structured data. Link prediction heuristics use some score functions, such as common neighbors and Katz index, to measure the likelihoo…
Recovering Metabolic Networks using A Novel Hyperlink Prediction Method
Muhan Zhang, Zhicheng Cui, Tolutola Oyetunde +2
Studying metabolic networks is vital for many areas such as novel drugs and bio-fuels. For biologists, a key challenge is that many reactions are impractical or expensive to be fou…