CLR, a novel contrastive learning method using graph-based sample relationships. This approach outperformed traditional ...

Given a set of training graphs, each associated with a class label, graph classification aims to learn a model from the training graphs to predict the unseen graphs in future. The following picture ...

Based on this cutting-edge technology, this paper conducts an in-depth study of graph data classification algorithms based on deep learning technology, focusing on two key aspects: attention network ...

Graphs are essential for modeling complex relationships, analyzing networks, and offering versatile representations that capture diverse data structures. Graph Neural Networks (GNNs) excel in ...

Benchmark dataset for graph classification This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks. The purpose of this ...

For the epileptic seizure classification task, the original EEG graph structure is iteratively optimized using a multi-head attention mechanism during training, rather than relying on a static, ...

The overarching goal of EEG data classification is to enable precise and automated analysis of brain activity, fostering applications in areas like brain-computer interfaces, sleep monitoring, ...

To be compliant, to ensure data is optimally protected, that it is available, that it can be analysed and that it is stored most cost-effectively – these are reasons why data classification is ...

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies ...