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 20
Benchmarking Positional Encodings for GNNs and Graph Transformers
Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs
GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
Guiding Word Equation Solving using Graph Neural Networks (Extended Technical Report)
Graph as a feature: improving node classification with non-neural graph-aware logistic regression
Continuous GNN-based Anomaly Detection on Edge using Efficient Adaptive Knowledge Graph Learning
ScaleNet: Scale Invariance Learning in Directed Graphs
Gaussian Mixture Models Based Augmentation Enhances GNN Generalization
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network