The results show that this classification algorithm, which combines graph neural network and LSTM, can better understand and utilize the topological structure information in graph data while ...

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 ...

We propose a simple and fast algorithm based on the spectral decomposition of graph Laplacian to perform graph classification and get a first reference score for a dataset. We show that this method ...

We use different dropout ratios (0.1–0.5) on the two graph node classification data sets of Cora and Citeseer, and conduct experiments on the neural network based on the LGNN4 algorithm to compare and ...

This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks. The purpose of this dataset is to make the features on the nodes and ...

We apply our framework to graph classification problems and obtain performances competitive with other persistence-based architectures. To provide the underlying theoretical foundations, we extend the ...

There are many types of graph clustering algorithms, such as hierarchical, spectral, modularity-based, and density-based algorithms. Hierarchical algorithms create a tree-like structure of ...

The weighted k-nearest neighbors (k-NN) classification algorithm is a relatively simple technique to predict the class of an item based on two or more numeric ... screenshot of a demo run in Figure 1 ...