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 - 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
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