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 22
MADE: Graph Backdoor Defense with Masked Unlearning
Instance-Aware Graph Prompt Learning
A Graph Neural Network deep-dive into successful counterattacks
Rewiring Techniques to Mitigate Oversquashing and Oversmoothing in GNNs: A Survey
GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network
Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning