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 ...
Sreesankar10/Analyzing-GNN-Models-for-Community-Detection-Using-Graph-Embeddings-A-Comparative-Study
This project focuses on the comparative analysis of Graph Neural Networks (GNNs) and traditional clustering algorithms for graph-structured data analysis. GNNs have emerged as powerful tools for ...
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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 ...
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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 ...
Introduction: Mild cognitive impairment (MCI), often linked to early neurodegeneration, is associated with subtle disruptions in brain connectivity. In this paper, the applicability of persistent ...
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