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 ...

Shirui Pan, Jia Wu, and Xingquan Zhu “CogBoost: Boosting for Fast Cost-sensitive Graph Classification", IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(11): 2933-2946 (2015) Shirui Pan, ...

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

Dataset for testing graph classification algorithms, such as Graph Kernels and Graph Neural Networks. - FilippoMB/Benchmark_dataset_for_graph_classification. Skip to content. ... an SVM that uses as ...

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

Graph analytics can be performed on any back end, as they only require reading graph-shaped data. Graph databases are databases with the ability to fully support both read and write, utilizing a ...

Keywords: seizure classification, GCN, iterative graph optimization, long-term dependencies in EEG series, imbalanced distribution. Citation: Hu Y, Liu J, Sun R, Yu Y and Sui Y (2024) Classification ...

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 ...

Each graph reflects the structural relationships among EEG data points within the corresponding frequency band. From these visibility graphs, we derive a comprehensive set of graph features, ...