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

August 29, 2024

Do Graph Neural Networks Work for High Entropy Alloys?
Hengrui Zhang, Ruishu Huang, Jie Chen, James M. Rondinelli, Wei Chen
Graph Neural Network Machine Learning Model Graph Representation Property Prediction High Entropy Alloy

August 28, 2024

August 27, 2024

August 26, 2024

August 25, 2024

August 24, 2024

GNN: Graph Neural Network and Large Language Model for Data Discovery
Thomas Hoang
Large Language Model Graph Neural Network Gene Level GNN GNN Based Data Driven Discovery Data Exploration

August 23, 2024

Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
Yeon-Chang Lee, Hojung Shin, Sang-Wook Kim
LeArning Abstract Graph Neural Network Graph Representation Learning Meaningful Representation Self Debiasing GNN Explanation Disentangle Content GNN Cl GNN Framework

August 22, 2024

August 20, 2024

GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization
Xiaodong Yang, Xiaoting Li, Huiyuan Chen, Yiwei Cai
Graph Neural Network Influence Maximization Go Game Adversarial Influence

Graph Neural Network

Papers

Do Graph Neural Networks Work for High Entropy Alloys?

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations

The Role of Fibration Symmetries in Geometric Deep Learning

Understanding GNNs for Boolean Satisfiability through Approximation Algorithms

Earth Observation Satellite Scheduling with Graph Neural Networks

Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning

Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies

Exploring the Potential of Large Language Models for Heterophilic Graphs

PAGE: Parametric Generative Explainer for Graph Neural Network

Optimizing Luxury Vehicle Dealership Networks: A Graph Neural Network Approach to Site Selection

Generalization of Graph Neural Networks is Robust to Model Mismatch

RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification

GNN: Graph Neural Network and Large Language Model for Data Discovery

Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks

Non-Homophilic Graph Pre-Training and Prompt Learning

Enhanced Expressivity in Graph Neural Networks with Lanczos-Based Linear Constraints

Optimizing Federated Graph Learning with Inherent Structural Knowledge and Dual-Densely Connected GNNs

Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks

Modeling Reference-dependent Choices with Graph Neural Networks

GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization