Search Results for "graphs"

The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. ...

Java generic API for search algorithms on graphs implicitly given by tree node expansion operator

Quantum walk library is set of tools for simulations of quantum walks (for chiral or achiral particles) on graphs based on Zarja library.

Chemin optimal allows to draw nodes on a scene and link them with Arcs. You can affect values to each arc (the path's length). By clicking the launch button, chemin optimal show you the optimal path of the graphs using FORD algorithm. All operations are shown step by step. You can export the scene to a XML file. You can also load scene freom existing XML file. Chemin optimal check errors automatically before finding the minimal path. Chemin optimal is written in JAVA( JAVAFX ).