grape
GRAPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both off-the-shelf laptop and desktop computers and High Performance Computing clusters of workstations. The library is written in Rust and Python programming languages and is composed of two main modules: Ensmallen (ENabler of SMALL runtimE and memory Needs) and Embiggen (EMBeddInG GENerator), that run synergistically using parallel computation and efficient data structures.
Reference Manual
View Source
""" [GRAPE](https://github.com/AnacletoLAB/grape) (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both off-the-shelf laptop and desktop computers and High Performance Computing clusters of workstations. The library is written in Rust and Python programming languages and is composed of two main modules: [Ensmallen](https://github.com/AnacletoLAB/ensmallen) (ENabler of SMALL runtimE and memory Needs) and [Embiggen](https://github.com/monarch-initiative/embiggen) (EMBeddInG GENerator), that run synergistically using parallel computation and efficient data structures. # Reference Manual """ from . import embiggen from . import ensmallen __all__ = [ "embiggen", "ensmallen" ]
View Source
"""Module with models for graph and text embedding and their Keras Sequences.""" from .embedders import ( CBOW, GloVe, GraphCBOW, GraphGloVe, GraphSkipGram, SkipGram, TransE, TransH, TransR, SimplE, Siamese ) from .node_prediction import GraphConvolutionalNeuralNetwork from .sequences import Word2VecSequence from .transformers import (CorpusTransformer, EdgeTransformer, GraphTransformer, LinkPredictionTransformer, NodeTransformer) from .visualizations import GraphVisualization __all__ = [ "CBOW", "SkipGram", "GloVe", "GraphCBOW", "GraphSkipGram", "GraphGloVe", "Word2VecSequence", "NodeTransformer", "EdgeTransformer", "GraphTransformer", "CorpusTransformer", "LinkPredictionTransformer", "GraphVisualization", "TransE", "TransH", "TransR", "SimplE", "Siamese", "GraphConvolutionalNeuralNetwork" ]
Module with models for graph and text embedding and their Keras Sequences.
View Source
""" [GRAPE](https://github.com/AnacletoLAB/grape) (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both off-the-shelf laptop and desktop computers and High Performance Computing clusters of workstations. The library is written in Rust and Python programming languages and is composed of two main modules: [Ensmallen](https://github.com/AnacletoLAB/ensmallen) (ENabler of SMALL runtimE and memory Needs) and [Embiggen](https://github.com/monarch-initiative/embiggen) (EMBeddInG GENerator), that run synergistically using parallel computation and efficient data structures. """ from .ensmallen import preprocessing # pylint: disable=import-error from .ensmallen import Graph # pylint: disable=import-error from .ensmallen import edge_list_utils # pylint: disable=import-error # The import of dataset should ALWAYS be under the imports from the compiled bindings # Because otherwise it generate a Circular import and crash from . import datasets __all__ = ["edge_list_utils", "Graph", "preprocessing", "datasets"]
GRAPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both off-the-shelf laptop and desktop computers and High Performance Computing clusters of workstations. The library is written in Rust and Python programming languages and is composed of two main modules: Ensmallen (ENabler of SMALL runtimE and memory Needs) and Embiggen (EMBeddInG GENerator), that run synergistically using parallel computation and efficient data structures.