Graph Neural Network

Graph Neural Networks (GNNs) are a class of machine learning models designed to analyze and learn from data represented as graphs, focusing on capturing relationships between nodes and their impact on downstream tasks like node classification and link prediction. Current research emphasizes improving GNN performance by addressing limitations such as oversmoothing and oversquashing through architectural innovations (e.g., incorporating residual connections, Cayley graph propagation) and novel training techniques (e.g., contrastive learning, Laplacian regularization). GNNs are proving valuable across diverse fields, including social network analysis, drug discovery, and financial modeling, offering powerful tools for analyzing complex relational data where traditional methods fall short.

Papers

October 22, 2024

Can Large Language Models Act as Ensembler for Multi-GNNs?
Hanqi Duan, Yao Cheng, Jianxiang Yu, Xiang Li
Graph Neural Network Text Attributed Graph LLM Ensemble HGNN Model
Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs
Haolin Li, Luana Ruiz
Graph Neural Network Homophilous Graph Citation Network Graph Sampling Expressive Graph Neural Network

October 21, 2024

Theoretical Insights into Line Graph Transformation on Graph Learning
Fan Yang, Xingyue Huang
Graph Neural Network Graph Representation Graph Learning Theoretical Analysis Graph Isomorphism Graph Transformation
Resilient Temporal GCN for Smart Grid State Estimation Under Topology Inaccuracies
Seyed Hamed Haghshenas, Mia Naeini
Graph Neural Network State Estimation Topological Feature Temporal Graph Topology Problem Factual Error Uncertain System Temporal GNN System State Estimation
Focus Where It Matters: Graph Selective State Focused Attention Networks
Shikhar Vashistha, Neetesh Kumar
Graph Neural Network Graph Attention Network Graph Structured Data Human Driving Focus Node Importance
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han, Qizhang Feng, Hongye Jin, Qiaoyu Tan, Kaixiong Zhou, Na Zou, Xia Hu
Graph Neural Network Model Editor

October 20, 2024

Tensor-Fused Multi-View Graph Contrastive Learning
Yujia Wu, Junyi Mo, Elynn Chen, Yuzhou Chen
Graph Neural Network Contrastive Learning Graph Contrastive Learning Contrastive Graph Rank Adaptive Tensor Optimization
Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
Yujia Wu, Bo Yang, Yang Zhao, Elynn Chen, Yuzhou Chen, Zheshi Zheng
Graph Neural Network Uncertainty Quantification Network Programming Tensor Decomposition Prediction Uncertainty

October 19, 2024

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
Yinhan He, Zaiyi Zheng, Patrick Soga, Yaozhen Zhu, yushun Dong, Jundong Li
Graph Neural Network Molecular Property Prediction Molecular Property Prediction Task Counterfactual Graph
Faster Inference Time for GNNs using coarsening
Shubhajit Roy, Hrriday Ruparel, Kishan Ved, Anirban Dasgupta
Graph Neural Network Large Scale Graph Subgraph GNN Graph Regression

October 18, 2024

Convergence of Manifold Filter-Combine Networks
David R. Johnson, Joyce Chew, Siddharth Viswanath, Edward De Brouwer, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter
Graph Neural Network Early Stage Convergence Non Linear Manifold Neural Manifold
Improving Graph Neural Networks by Learning Continuous Edge Directions
Seong Ho Pahng, Sahand Hormoz
Graph Neural Network Node Classification Graph Structure Undirected Graph
DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks
Hao Sui, Bing Chen, Jiale Zhang, Chengcheng Zhu, Di Wu, Qinghua Lu, Guodong Long
Graph Neural Network Adversarial Attack Network Programming Graph Neural Stealthy Backdoor Attack Mitigating Backdoor Graph Backdoor Attack

October 17, 2024

Trojan Prompt Attacks on Graph Neural Networks
Minhua Lin, Zhiwei Zhang, Enyan Dai, Zongyu Wu, Yilong Wang, Xiang Zhang, Suhang Wang
Graph Neural Network Backdoor Attack Prompt Learning Trojan Attack Graph Backdoor Attack
Learning Graph Quantized Tokenizers for Transformers
Limei Wang, Kaveh Hassani, Si Zhang, Dongqi Fu, Baichuan Yuan, Weilin Cong, Zhigang Hua, Hao Wu, Ning Yao, Bo Long
Graph Neural Network Transformer Megatron Decepticons Transformer Encoder Transformer Training
Addressing Heterogeneity and Heterophily in Graphs: A Heterogeneous Heterophilic Spectral Graph Neural Network
Kangkang Lu, Yanhua Yu, Zhiyong Huang, Jia Li, Yuling Wang, Meiyu Liang, Xiting Qin, Yimeng Ren, Tat-Seng Chua, Xidian Wang
Graph Neural Network Graph Drawing Heterogeneous Graph Heterophilic Graph Spectral Graph Neural Network Heterogeneous Setting Heterogeneity Challenge

October 16, 2024

Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networks
Kaiwen Xia, Huijun Wu, Duanyu Li, Min Xie, Ruibo Wang, Wenzhe Zhang
Graph Neural Network Adversarial Attack Adversarial Perturbation Curriculum Learning
Federated Temporal Graph Clustering
Yang Liu, Zihao Zhou, Xianghong Xu, Qian Li
Graph Neural Network Temporal Graph Temporal Aggregation Temporal Graph Clustering
Learning Differentiable Tensegrity Dynamics using Graph Neural Networks
Nelson Chen, Kun Wang, William R. Johnson III, Rebecca Kramer-Bottiglio, Kostas Bekris, Mridul Aanjaneya
Graph Neural Network Tensegrity Robot Physic Engine Soft Rigid
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Layers of Interpretability
Gihan Panapitiya, Peiyuan Gao, C Mark Maupin, Emily G Saldanha
Graph Neural Network Inherent Interpretability Multi Layer Molecular Property Prediction Molecular Property Molecular Structure Molecular Fragment IARS Segnet

Graph Neural Network

Papers

Can Large Language Models Act as Ensembler for Multi-GNNs?

Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs

Theoretical Insights into Line Graph Transformation on Graph Learning

Resilient Temporal GCN for Smart Grid State Estimation Under Topology Inaccuracies

Focus Where It Matters: Graph Selective State Focused Attention Networks

Gradient Rewiring for Editable Graph Neural Network Training

Tensor-Fused Multi-View Graph Contrastive Learning

Conditional Uncertainty Quantification for Tensorized Topological Neural Networks

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction

Faster Inference Time for GNNs using coarsening

Convergence of Manifold Filter-Combine Networks

Improving Graph Neural Networks by Learning Continuous Edge Directions

DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks

Trojan Prompt Attacks on Graph Neural Networks

Learning Graph Quantized Tokenizers for Transformers

Addressing Heterogeneity and Heterophily in Graphs: A Heterogeneous Heterophilic Spectral Graph Neural Network

Perseus: Leveraging Common Data Patterns with Curriculum Learning for More Robust Graph Neural Networks

Federated Temporal Graph Clustering

Learning Differentiable Tensegrity Dynamics using Graph Neural Networks

FragNet: A Graph Neural Network for Molecular Property Prediction with Four Layers of Interpretability