59 citations · 387 across the 30 of their papers we have counts for
11 papers · 1 filter
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
Junru Zhou, Jiarui Feng, Xiyuan Wang +1
The ability of graph neural networks (GNNs) to count certain graph substructures, especially cycles, is important for the success of GNNs on a wide range of tasks. It has been rece…
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
Ling Yang, Ye Tian, Minkai Xu +7
GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output re…
Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners
Xiaojuan Tang, Zilong Zheng, Jiaqi Li +4
The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years. Despite of numerous…
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
Cai Zhou, Xiyuan Wang, Muhan Zhang
Relational pooling is a framework for building more expressive and permutation-invariant graph neural networks. However, there is limited understanding of the exact enhancement in…
Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models
Yi Hu, Haotong Yang, Zhouchen Lin +1
Large language models (LLMs) have scaled up to unlock a wide range of complex reasoning tasks with the aid of various prompting methods. However, current prompting methods generate…
Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick
Xiyuan Wang, Pan Li, Muhan Zhang
In this paper, we study using graph neural networks (GNNs) for \textit{multi-node representation learning}, where a representation for a set of more than one node (such as a link)…
An Empirical Study of Realized GNN Expressiveness
Yanbo Wang, Muhan Zhang
Research on the theoretical expressiveness of Graph Neural Networks (GNNs) has developed rapidly, and many methods have been proposed to enhance the expressiveness. However, most m…
An Efficient Subgraph GNN with Provable Substructure Counting Power
Zuoyu Yan, Junru Zhou, Liangcai Gao +2
We investigate the enhancement of graph neural networks' (GNNs) representation power through their ability in substructure counting. Recent advances have seen the adoption of subgr…
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
Haoteng Yin, Muhan Zhang, Jianguo Wang +1
Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and gen…
Is Distance Matrix Enough for Geometric Deep Learning?
Zian Li, Xiyuan Wang, Yinan Huang +1
Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into…
Neural Common Neighbor with Completion for Link Prediction
Xiyuan Wang, Haotong Yang, Muhan Zhang
In this work, we propose a novel link prediction model and further boost it by studying graph incompleteness. First, we introduce MPNN-then-SF, an innovative architecture leveragin…