59 citations · 387 across the 30 of their papers we have counts for
12 papers · 1 filter
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…
RulE: Knowledge Graph Reasoning with Rule Embedding
Xiaojuan Tang, Song-Chun Zhu, Yitao Liang +1
Knowledge graph (KG) reasoning is an important problem for knowledge graphs. In this paper, we propose a novel and principled framework called \textbf{RulE} (stands for {Rul}e {E}m…
Boosting the Cycle Counting Power of Graph Neural Networks with I-GNNs
Yinan Huang, Xingang Peng, Jianzhu Ma +1
Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful…
Geodesic Graph Neural Network for Efficient Graph Representation Learning
Lecheng Kong, Yixin Chen, Muhan Zhang
Graph Neural Networks (GNNs) have recently been applied to graph learning tasks and achieved state-of-the-art (SOTA) results. However, many competitive methods run GNNs multiple ti…
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…
Graph Neural Network with Local Frame for Molecular Potential Energy Surface
Xiyuan Wang, Muhan Zhang
Modeling molecular potential energy surface is of pivotal importance in science. Graph Neural Networks have shown great success in this field. However, their message passing scheme…
Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction
Yang Hu, Xiyuan Wang, Zhouchen Lin +2
Link prediction is one important application of graph neural networks (GNNs). Most existing GNNs for link prediction are based on one-dimensional Weisfeiler-Lehman (1-WL) test. 1-W…
How Powerful are Spectral Graph Neural Networks
Xiyuan Wang, Muhan Zhang
Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on graph signal filters. Some models able to learn arbitrary spectral filters have emerged recently. How…
How Powerful are K-hop Message Passing Graph Neural Networks
Jiarui Feng, Yixin Chen, Fuhai Li +2
The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message passing -- aggregating information from 1-hop neighbors repeatedly. However, the expressive power…
Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Haorui Wang, Haoteng Yin, Muhan Zhang +1
Graph neural networks (GNN) have shown great advantages in many graph-based learning tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif…
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Haoteng Yin, Muhan Zhang, Yanbang Wang +2
Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and h…
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…