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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.

The project contains algorithms for locating the most central groups in complex networks. In particular implementations of various centrality measures and heuristic search algorithms. All functionality is exposed via XML-RPC for easier exploitation.

Yabman is a tool for managing bibliographic references. Its key features are a quality user interface, a carefully designed data model, and sophisticated three-state hierarchical reference labeling. It is currently usable but in a pre-alpha stage.