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
LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection
From Text to Graph: Leveraging Graph Neural Networks for Enhanced Explainability in NLP
Geometric Reasoning in the Embedding Space
Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering
Refining Interactions: Enhancing Anisotropy in Graph Neural Networks with Language Semantics
Lorentzian Graph Isomorphic Network
Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning
GNN-Based Candidate Node Predictor for Influence Maximization in Temporal Graphs