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

March 25, 2024

Manufacturing Service Capability Prediction with Graph Neural Networks
Yunqing Li, Xiaorui Liu, Binil Starly
Graph Neural Network Service Discovery
Less Is More -- On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP
Attila Lischka, Jiaming Wu, Rafael Basso, Morteza Haghir Chehreghani, Balázs Kulcsár
Graph Neural Network Transformer Megatron Decepticons Graph Representation Importance Aware Random Sparsification Transformer Encoder Traveling Salesman Problem Graph Sparsification

March 24, 2024

SSHPool: The Separated Subgraph-based Hierarchical Pooling
Zhuo Xu, Lixin Cui, Ming Li, Yue Wang, Ziyu Lyu, Hangyuan Du, Lu Bai, Philip S. Yu, Edwin R. Hancock
Graph Neural Network Graph Classification Pooling Layer Graph Pooling
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long
Graph Neural Network Graph Transformer Graph Data Virtual Network Graphormer Reactivity Model

March 22, 2024

GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation
Yundong Sun, Dongjie Zhu, Yansong Wang, Zhaoshuo Tian
Graph Neural Network Graph Representation Heterogeneous Graph Co Contrastive Learning
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Sukhdeep Singh, Anuj Sharma, Vinod Kumar Chauhan
Graph Neural Network Graph Classification Graph Structured Data Adaptive Graph Aggregation Network
Simple Graph Condensation
Zhenbang Xiao, Yu Wang, Shunyu Liu, Huiqiong Wang, Mingli Song, Tongya Zheng
Graph Neural Network Graph Condensation Graph Condensation Method

March 21, 2024

iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
Md Saidul Hoque Anik, Pranav Badhe, Rohit Gampa, Ariful Azad
Graph Neural Network Pytorch Model Easy to Use Library Sparse Operation Dense Matrix Multiplication
NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks
Yi-Shan Lan, Pin-Yu Chen, Tsung-Yi Ho
Graph Neural Network Data Augmentation Molecular Conformation Protein Structure Protein Classification
SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention
Yundong Sun, Dongjie Zhu, Yansong Wang, Zhaoshuo Tian, Ning Cao, Gregory O'Hared
Graph Neural Network Graph Transformer Graph Representation Graph Attention

March 19, 2024

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites
Marko Petković, José Manuel Vicent-Luna, Vlado Menkovski, Sofía Calero
Graph Neural Network Molecular Simulation Adsorption Energy Pressure Swing Adsorption
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
Cheng Yang, Jixi Liu, Yunhe Yan, Chuan Shi
Graph Neural Network Graph Structured Data Fairness Metric Achieving Fairness GNN Backbone

March 18, 2024

Molecular Classification Using Hyperdimensional Graph Classification
Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis, Alexandru Nicolau
Graph Neural Network Graph Representation Graph Learning Hyperdimensional Computing
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Miltiadis Kofinas, Boris Knyazev, Yan Zhang, Yunlu Chen, Gertjan J. Burghouts, Efstratios Gavves, Cees G. M. Snoek, David W. Zhang
Neural Network Graph Neural Network Generalization Performance Neural Network Weight Equivariant Representation Learning
Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks
K. P. Santoso, R. V. H. Ginardi, R. A. Sastrowardoyo, F. A. Madany
Graph Neural Network Spatial Domain Skin Lesion Capsule Network
NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction
V Hewes, Adam Aurisano, Giuseppe Cerati, Jim Kowalkowski, Claire Lee, Wei-keng Liao, Daniel Grzenda, Kaushal Gumpula, Xiaohe Zhang
Graph Neural Network State of the Art Detector Neutrino Physic Particle Reconstruction Neutrino Telescope
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks
Andrea Venturi, Dario Stabili, Mirco Marchetti
Graph Neural Network Adversarial Attack Network Intrusion Detection Open Problem Adversarial Objective Attack Graph Structural Attack

Graph Neural Network

Papers

Manufacturing Service Capability Prediction with Graph Neural Networks

Less Is More -- On the Importance of Sparsification for Transformers and Graph Neural Networks for TSP

SSHPool: The Separated Subgraph-based Hierarchical Pooling

VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections

GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation

GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks

Simple Graph Condensation

iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations

NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks

SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention

Sparse Implementation of Versatile Graph-Informed Layers

Graph Neural Network for Crawling Target Nodes in Social Networks

Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network

Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged Zeolites

FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization

Molecular Classification Using Hyperdimensional Graph Classification

Graph Neural Networks for Learning Equivariant Representations of Neural Networks

Leveraging Spatial and Semantic Feature Extraction for Skin Cancer Diagnosis with Capsule Networks and Graph Neural Networks

NuGraph2: A Graph Neural Network for Neutrino Physics Event Reconstruction

Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks