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...
share
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...
research
∙
08/31/2023
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
The electronic design automation of analog circuits has been a longstand...
research
∙
08/04/2023
VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs
Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
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...
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...
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...
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...
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)...
research
∙
04/16/2023
Towards Better Evaluation of GNN Expressiveness with BREC Dataset
Research on the theoretical expressiveness of Graph Neural Networks (GNN...
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...
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...
research
∙
03/05/2023
Time Associated Meta Learning for Clinical Prediction
Rich Electronic Health Records (EHR), have created opportunities to impr...
research
∙
02/11/2023
Is Distance Matrix Enough for Geometric Deep Learning?
Graph Neural Networks (GNNs) are often used for tasks involving the geom...
research
∙
02/02/2023
Neural Common Neighbor with Completion for Link Prediction
Despite its outstanding performance in various graph tasks, vanilla Mess...
research
∙
10/24/2022
RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding
Knowledge graph (KG) reasoning is an important problem for knowledge gra...
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...
research
∙
10/06/2022
Geodesic Graph Neural Network for Efficient Graph Representation Learning
Recently, Graph Neural Networks (GNNs) have been applied to graph learni...
research
∙
09/19/2022
Rethinking Knowledge Graph Evaluation Under the Open-World Assumption
Most knowledge graphs (KGs) are incomplete, which motivates one importan...
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 ...
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...
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...
research
∙
05/23/2022
How Powerful are Spectral Graph Neural Networks
Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) ba...
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...
research
∙
03/19/2022
PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs
Optimization of directed acyclic graph (DAG) structures has many applica...
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...
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...
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...
research
∙
12/14/2021
Efficient Dynamic Graph Representation Learning at Scale
Dynamic graphs with ordered sequences of events between nodes are preval...
research
∙
11/23/2021
Network In Graph Neural Network
Graph Neural Networks (GNNs) have shown success in learning from graph s...
research
∙
10/25/2021
Nested Graph Neural Networks
Graph neural network (GNN)'s success in graph classification is closely ...
research
∙
06/08/2021
Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
Hypergraph offers a framework to depict the multilateral relationships i...
research
∙
12/02/2020
Deep Graph Neural Networks with Shallow Subgraph Samplers
While Graph Neural Networks (GNNs) are powerful models for learning repr...
research
∙
10/30/2020
Revisiting Graph Neural Networks for Link Prediction
Graph neural networks (GNNs) have achieved great success in recent years...
research
∙
07/29/2020
Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis
Understanding how certain brain regions relate to a specific neurologica...
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...
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...
research
∙
02/27/2018