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 4, 2024

Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective
Junru Zhou, Muhan Zhang
Graph Neural Network GNN Model Expressive Power Weisfeiler Leman Expressive Graph Neural Network Homomorphism Count
Cayley Graph Propagation
JJ Wilson, Maya Bechler-Speicher, Petar Veličković
Graph Neural Network Graph Propagation Expander Graph

October 3, 2024

Language Models are Graph Learners
Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
Language Model Graph Neural Network Graph Transformer Graph Learning Graph Learning Task Graph Learning Model
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations
Md Joshem Uddin, Astrit Tola, Varin Sikand, Cuneyt Gurcan Akcora, Baris Coskunuzer
Graph Neural Network Graph Representation Node Classification Node Representation Node Embeddings Spatial Domain True Class Semantic Gap Energy Landscape

October 2, 2024

Is uniform expressivity too restrictive? Towards efficient expressivity of graph neural networks
Sammy Khalife, Josué Tonelli-Cueto
Graph Neural Network Gene Level GNN Large Graph Expressivity Transfer Expressivity Matter
PROXI: Challenging the GNNs for Link Prediction
Astrit Tola, Jack Myrick, Baris Coskunuzer
Graph Neural Network Graph Representation Learning Link Prediction Heterophilic Graph
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
Xingyu Ji, Jiale Liu, Lu Li, Maojun Wang, Zeyu Zhang
Graph Neural Network Graph Representation Learning Graph Representation Complex Process Graph Machine Learning Text Attributed Graph Interpretable Graph
Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency
Diego Vallarino
Graph Neural Network High Frequency Hybrid Model Cost Effective Portfolio Optimization Dynamic Rebalancing
Rethinking the Expressiveness of GNNs: A Computational Model Perspective
Guanyu Cui, Zhewei Wei, Hsin-Hao Su
Graph Neural Network Graph Machine Learning Expressive Speech Message Passing Graph Neural Network Expressive GNN Order Graph Neural Network
"No Matter What You Do!": Mitigating Backdoor Attacks in Graph Neural Networks
Jiale Zhang, Chengcheng Zhu, Bosen Rao, Hao Sui, Xiaobing Sun, Bing Chen, Chunyi Zhou, Shouling Ji
Graph Neural Network Backdoor Attack Backdoor Defense Backdoor Learning Backdoor Removal

October 1, 2024

Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks
Amy Ancelotti, Claudia Liason
Convolutional Neural Network Graph Neural Network Narrative Review Blockchain Based Platform Blockchain Network Blockchain Transaction
WiGNet: Windowed Vision Graph Neural Network
Gabriele Spadaro, Marco Grangetto, Attilio Fiandrotti, Enzo Tartaglione, Jhony H. Giraldo
Graph Neural Network Graph Convolution GNN Based Service Vision Graph
Cross-Camera Data Association via GNN for Supervised Graph Clustering
Đorđe Nedeljković
Graph Neural Network Gene Level GNN Multi Camera Graph Clustering Pedestrian Data GNN Cl Multi Camera Association
LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN
Yuxing Zhang, Siyuan Meng, Chunchun Chen, Mengyao Peng, Hongyan Gu, Xinli Huang
Graph Neural Network Gene Level GNN Tiltable Link Node Similarity Shadow Model URL Classification Generalized Knowledge Linkage Attack Target Graph

September 30, 2024

Computing Systemic Risk Measures with Graph Neural Networks
Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber
Graph Neural Network Equivariant Neural Network Optimal Allocation Financial Network Systemic Risk Measure
geom2vec: pretrained GNNs as geometric featurizers for conformational dynamics
Zihan Pengmei, Chatipat Lorpaiboon, Spencer C. Guo, Jonathan Weare, Aaron R. Dinner
Graph Neural Network Molecular Graph Geometric Feature Molecular Simulation

September 29, 2024

Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning
Shreyas Muthusamy, Damian Owerko, Charilaos I. Kanatsoulis, Saurav Agarwal, Alejandro Ribeiro
Graph Neural Network Multi Robot System Nearest Neighbor Stronger Generalizability Multi Robot Coordination Decentralized Path Planning
A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
Yucheng Wang, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen
Graph Neural Network Global Evaluation NCD Method Prognostic Prediction Useful Life Future Trend Remaining Useful Remaining Useful Life Prediction
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
Zeyu Zhang, Lu Li, Shuyan Wan, Sijie Wang, Zhiyi Wang, Zhiyuan Lu, Dong Hao, Wanli Li
Graph Neural Network Graph Augmentation Effective Augmentation Link Sign Prediction
DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
K. Mancini, I. Rekik
Graph Neural Network Graph Condensation Graph Information Bottleneck

Graph Neural Network

Papers

Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective

Cayley Graph Propagation

Language Models are Graph Learners

ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations

Is uniform expressivity too restrictive? Towards efficient expressivity of graph neural networks

PROXI: Challenging the GNNs for Link Prediction

Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process

Dynamic Portfolio Rebalancing: A Hybrid new Model Using GNNs and Pathfinding for Cost Efficiency

Rethinking the Expressiveness of GNNs: A Computational Model Perspective

"No Matter What You Do!": Mitigating Backdoor Attacks in Graph Neural Networks

Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks

WiGNet: Windowed Vision Graph Neural Network

Cross-Camera Data Association via GNN for Supervised Graph Clustering

LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN

Computing Systemic Risk Measures with Graph Neural Networks

geom2vec: pretrained GNNs as geometric featurizers for conformational dynamics

Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning

A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends

DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks

DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling