b'Muhan Zhang'

research

∙ 09/19/2023

Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks

Graph Neural Networks (GNNs) have become popular in Graph Representation...

0 Hao Liu, et al. ∙

research

∙ 09/10/2023

Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power

The ability of graph neural networks (GNNs) to count certain graph subst...

0 Junru Zhou, et al. ∙

research

∙ 08/31/2023

CktGNN: Circuit Graph Neural Network for Electronic Design Automation

The electronic design automation of analog circuits has been a longstand...

0 Zehao Dong, et al. ∙

research

∙ 08/04/2023

VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs

Graph Neural Networks (GNNs) conduct message passing which aggregates lo...

0 Ling Yang, et al. ∙

research

∙ 06/05/2023

Towards Arbitrarily Expressive GNNs in O(n^2) Space by Rethinking Folklore Weisfeiler-Lehman

Message passing neural networks (MPNNs) have emerged as the most popular...

0 Jiarui Feng, et al. ∙

research

∙ 05/29/2023

Code Prompting: a Neural Symbolic Method for Complex Reasoning in Large Language Models

Large language models (LLMs) have scaled up to unlock a wide range of co...

0 Yi Hu, et al. ∙

research

∙ 05/24/2023

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (L...

0 Xiaojuan Tang, et al. ∙

research

∙ 05/08/2023

From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks

Relational pooling is a framework for building more expressive and permu...

0 Cai Zhou, et al. ∙

research

∙ 04/20/2023

Improving Graph Neural Networks on Multi-node Tasks with Labeling Tricks

In this paper, we provide a theory of using graph neural networks (GNNs)...

0 Xiyuan Wang, et al. ∙

research

∙ 04/16/2023

Towards Better Evaluation of GNN Expressiveness with BREC Dataset

Research on the theoretical expressiveness of Graph Neural Networks (GNN...

0 Yanbo Wang, et al. ∙

research

∙ 03/19/2023

Efficiently Counting Substructures by Subgraph GNNs without Running GNN on Subgraphs

Using graph neural networks (GNNs) to approximate specific functions suc...

0 Zuoyu Yan, et al. ∙

research

∙ 03/06/2023

SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning

Subgraph-based graph representation learning (SGRL) has recently emerged...

0 Haoteng Yin, et al. ∙

research

∙ 03/05/2023

Time Associated Meta Learning for Clinical Prediction

Rich Electronic Health Records (EHR), have created opportunities to impr...

0 Hao Liu, et al. ∙

research

∙ 02/11/2023

Is Distance Matrix Enough for Geometric Deep Learning?

Graph Neural Networks (GNNs) are often used for tasks involving the geom...

0 Zian Li, et al. ∙

research

∙ 02/02/2023

Neural Common Neighbor with Completion for Link Prediction

Despite its outstanding performance in various graph tasks, vanilla Mess...

0 Xiyuan Wang, et al. ∙

research

∙ 10/24/2022

RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding

Knowledge graph (KG) reasoning is an important problem for knowledge gra...

0 Xiaojuan Tang, et al. ∙

research

∙ 10/07/2022

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

Recent years have seen advances on principles and guidance relating to a...

0 Hao Wang, et al. ∙

research

∙ 10/06/2022

Geodesic Graph Neural Network for Efficient Graph Representation Learning

Recently, Graph Neural Networks (GNNs) have been applied to graph learni...

0 Lecheng Kong, et al. ∙

research

∙ 09/19/2022

Rethinking Knowledge Graph Evaluation Under the Open-World Assumption

Most knowledge graphs (KGs) are incomplete, which motivates one importan...

0 Haotong Yang, et al. ∙

research

∙ 08/01/2022

Graph Neural Network with Local Frame for Molecular Potential Energy Surface

Modeling molecular potential energy surface is of pivotal importance in ...

0 Xiyuan Wang, et al. ∙

research

∙ 06/20/2022

Two-Dimensional Weisfeiler-Lehman Graph Neural Networks for Link Prediction

Link prediction is one important application of graph neural networks (G...

11 Yang Hu, et al. ∙

research

∙ 05/26/2022

How Powerful are K-hop Message Passing Graph Neural Networks

The most popular design paradigm for Graph Neural Networks (GNNs) is 1-h...

0 Jiarui Feng, et al. ∙

research

∙ 05/23/2022

How Powerful are Spectral Graph Neural Networks

Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) ba...

0 Xiyuan Wang, et al. ∙

research

∙ 05/15/2022

3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design

Deep learning has achieved tremendous success in designing novel chemica...

0 Yinan Huang, et al. ∙

research

∙ 03/19/2022

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs

Optimization of directed acyclic graph (DAG) structures has many applica...

0 Zehao Dong, et al. ∙

research

∙ 03/01/2022

Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks

Graph neural networks (GNN) have shown great advantages in many graph-ba...

2 Haorui Wang, et al. ∙

research

∙ 02/28/2022

Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning

Subgraph-based graph representation learning (SGRL) has been recently pr...

3 Haoteng Yin, et al. ∙

research

∙ 01/19/2022

Decoupling the Depth and Scope of Graph Neural Networks

State-of-the-art Graph Neural Networks (GNNs) have limited scalability w...

0 Hanqing Zeng, et al. ∙

research

∙ 12/14/2021

Efficient Dynamic Graph Representation Learning at Scale

Dynamic graphs with ordered sequences of events between nodes are preval...

7 Xinshi Chen, et al. ∙

research

∙ 11/23/2021

Network In Graph Neural Network

Graph Neural Networks (GNNs) have shown success in learning from graph s...

0 Xiang Song, et al. ∙

research

∙ 10/25/2021

Nested Graph Neural Networks

Graph neural network (GNN)'s success in graph classification is closely ...

0 Muhan Zhang, et al. ∙

research

∙ 06/08/2021

Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks

Hypergraph offers a framework to depict the multilateral relationships i...

0 Changlin Wan, et al. ∙

research

∙ 12/02/2020

Deep Graph Neural Networks with Shallow Subgraph Samplers

While Graph Neural Networks (GNNs) are powerful models for learning repr...

0 Hanqing Zeng, et al. ∙

research

∙ 10/30/2020

Revisiting Graph Neural Networks for Link Prediction

Graph neural networks (GNNs) have achieved great success in recent years...

0 Muhan Zhang, et al. ∙

research

∙ 07/29/2020

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Understanding how certain brain regions relate to a specific neurologica...

26 Xiaoxiao Li, et al. ∙

research

∙ 04/26/2019

Inductive Graph Pattern Learning for Recommender Systems Based on a Graph Neural Network

Most modern successful recommender systems are based on matrix factoriza...

0 Muhan Zhang, et al. ∙

research

∙ 04/24/2019

D-VAE: A Variational Autoencoder for Directed Acyclic Graphs

Graph structured data are abundant in the real world. Among different gr...

0 Muhan Zhang, et al. ∙

research

∙ 02/27/2018

Link Prediction Based on Graph Neural Networks

Traditional methods for link prediction can be categorized into three ma...

0 Muhan Zhang, et al. ∙

Muhan Zhang

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