This is a comprehensive implementation of a Graph-based Retrieval-Augmented Generation (RAG) System that demonstrates and compares multiple graph traversal strategies. The project showcases how ...

This project examines the performance of several Graph Neural Network (GNN) versions, such as Graph Autoencoders, Graph Convolutional Networks, Graph Attention Networks, and Graph Sample and ...

Abstract: In the modern world, all real-life problems, such as road networks, telecommunication networks, recommendation systems, social network interactions and so on can be modeled using Graph Data ...

Abstract: Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks ...