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.

2140papers

Papers - Page 7

February 15, 2025

LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs
Text Attributed Graph Temporal Representation Graph Neural Network Knowledge Distillation Link Prediction
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Graph Neural Network Representation Collapse Undisciplined Over Smoothing Bridging Text Gradient Vanishing Gene Level GNN Graph Learning

February 13, 2025

Enhancing the Utility of Higher-Order Information in Relational Learning
Relational Learning Hypergraph Structure Graph Neural Network Task Utility
Graph Diffusion Network for Drug-Gene Prediction
Graph Neural Heterogeneous Graph Learning Drug Disease Association Drug Target Interaction Prediction Graph Neural Network
LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection
Adversarial Attack Graph Neural Network Graph Adversarial Attack Link Recommendation GNN Based Service Natural Adversarial Adversarial Objective
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Efficient Architecture Gene Level GNN Graph Transformer Full Potential Graph Neural Network GNN Architecture Graph Level Task
Self-Supervised Graph Contrastive Pretraining for Device-level Integrated Circuits
Graph Contrastive Learning Message Passing Neural Network Digital Circuit Graph Representation Learning Graph Neural Network

February 12, 2025

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation
New Framework Label Propagation Graph Neural Network Transferable Representation Graph Regularized Tensor Domain Adaptation
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy
Graph Neural Network Semantic Understanding Comprehensive Taxonomy Trustworthy Graph Medical LLM Large Language Model Systematic Review Model Trustworthiness
Data Pricing for Graph Neural Networks without Pre-purchased Inspection
Data Pricing Graph Neural Network Peer to Peer Trading Visual Inspection Machine Learning Model
Equivariant Masked Position Prediction for Efficient Molecular Representation
Graph Neural Network Position Prediction Individual Representation Equivariant Generative Molecular Data
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
Graph Neural Graph Neural Architecture Search LLM Graph Architecture Search Graph Neural Network
Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification
Graph Neural Network Mixture Component Node Classification Message Passing Diverse Graph

Graph Neural Network

Papers - Page 7

Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs

A GNN-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin

Structure based SAT dataset for analysing GNN generalisation

Oversmoothing as Loss of Sign: Towards Structural Balance in Graph Neural Networks

LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs

On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning

COMBINEX: A Unified Counterfactual Explainer for Graph Neural Networks via Node Feature and Structural Perturbations

TransGUNet: Transformer Meets Graph-based Skip Connection for Medical Image Segmentation

Evaluating and Improving Graph-based Explanation Methods for Multi-Agent Coordination

Enhancing the Utility of Higher-Order Information in Relational Learning

Graph Diffusion Network for Drug-Gene Prediction

LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via Subgraph Injection

Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence

Self-Supervised Graph Contrastive Pretraining for Device-level Integrated Circuits

Bridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation

Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy

Data Pricing for Graph Neural Networks without Pre-purchased Inspection

Equivariant Masked Position Prediction for Efficient Molecular Representation

LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search

Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification