activity
20192022
most citedDecoupling the Depth and Scope of Graph Neural Networks

54 citations · 91 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ML2022

1st ICLR International Workshop on Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data (PAIR^2Struct)

Hao Wang, Wanyu Lin, Hao He +3

Recent years have seen advances on principles and guidance relating to accountable and ethical use of artificial intelligence (AI) spring up around the globe. Specifically, Data Pr…

cs.AI20224 cited

Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

Haotong Yang, Zhouchen Lin, Muhan Zhang

Most knowledge graphs (KGs) are incomplete, which motivates one important research topic on automatically complementing knowledge graphs. However, evaluation of knowledge graph com…

cs.LG202254 cited

Decoupling the Depth and Scope of Graph Neural Networks

Hanqing Zeng, Muhan Zhang, Yinglong Xia +6

State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes. On large graphs, increasing the model depth often means exponentia…

cs.LG202133 cited

Nested Graph Neural Networks

Muhan Zhang, Pan Li

Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a…

cs.CV2020

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek +4

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient…

cs.IR2019

Inductive Matrix Completion Based on Graph Neural Networks

Muhan Zhang, Yixin Chen

We propose an inductive matrix completion model without using side information. By factorizing the (rating) matrix into the product of low-dimensional latent embeddings of rows (us…