Graph-Based Classification: implementing a GNN that can classify entire graphs while dealing with an interesting dataset. Exploring different graph-level aggregation techniques, analyzing class ...
A comprehensive, production-ready implementation of Graph Isomorphism Networks (GIN) for graph classification tasks. This project provides a clean, reproducible, and showcase-ready implementation with ...
Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...
Abstract: With the rapid development of mobile Internet in recent years, a large scale of continuous arrival correlative data, namely dynamic streaming graph, are extensively generated in various ...
Respiratory sound classification plays a crucial role in the early diagnosis of respiratory diseases. In recent years, Convolutional Neural Networks (CNNs) have emerged as the dominant approach for ...
Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
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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