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updated to new doc theme (#1793)
Authors: - Brad Rees (https://github.com/BradReesWork) Approvers: - AJ Schmidt (https://github.com/ajschmidt8) - Rick Ratzel (https://github.com/rlratzel) URL: #1793
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conda/environments/cugraph_dev_cuda11.4.yml

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- faiss-proc=*=cuda
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- scikit-learn>=0.23.1
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- sphinx
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- sphinx_rtd_theme
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- pydata-sphinx-theme
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- sphinxcontrib-websupport
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- sphinx-markdown-tables
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- sphinx-copybutton

docs/cugraph/source/api_docs/structure.rst

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=========
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Structure
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=========
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.. currentmodule:: cugraph.structure.graph_classes
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=============
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Graph Classes
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=============
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.. currentmodule:: cugraph
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Constructor
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-----------
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Constructors
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------------
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.. autosummary::
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:toctree: api/
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Graph
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MultiGraph
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BiPartiteGraph
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Methods
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-------
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Adding Data
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-----------
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.. autosummary::
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:toctree: api/
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Graph.add_internal_vertex_id
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Graph.add_nodes_from
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Graph.clear
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Graph.from_cudf_adjlist
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Graph.from_cudf_edgelist
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Graph.from_dask_cudf_edgelist
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Graph.from_numpy_array
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Graph.from_numpy_matrix
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Graph.from_pandas_adjacency
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Graph.from_pandas_edgelist
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Graph.from_numpy_array
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Graph.from_numpy_matrix
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Graph.add_internal_vertex_id
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Graph.add_nodes_from
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Graph.clear
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Checks
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------
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.. autosummary::
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:toctree: api/
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Graph.has_isolated_vertices
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Graph.is_bipartite
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Graph.is_directed
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Graph.to_undirected
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Graph.unrenumber
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.. currentmodule:: cugraph
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Symmetrize
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----------

docs/cugraph/source/basics/cugraph_blogs.rst

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* `Where should I walk <https://towardsdatascience.com/where-should-i-walk-e66b26735de5>`_
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* `Where really are the parking spots? <https://towardsdatascience.com/where-really-are-the-parking-spots-ed6a1129035e>`_
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* `Accelerating Single Cell Genomic Analysis using RAPIDS <https://developer.nvidia.com/blog/accelerating-single-cell-genomic-analysis-using-rapids/>`_
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Copyright (c) 2020, NVIDIA CORPORATION.

docs/cugraph/source/index.rst

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Welcome to cugraph's documentation!
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===================================
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RAPIDS cuGraph is a library of graph algorithms that seamlessly integrates
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into the RAPIDS data science ecosystem and allows the data scientist to
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easily call graph algorithms using data stored in GPU DataFrames,
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NetworkX Graphs, or even CuPy or SciPy sparse Matrices.
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.. toctree::
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:maxdepth: 2

python/cugraph/cugraph/generators/rmat.py

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"""
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Ensures the args passed in are usable for the multi_rmat() API, raises the
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appropriate exception if incorrect, else returns None.
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"""
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if not isinstance(n_edgelists, int):
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raise TypeError("'n_edgelists' must be an int")
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scramble_vertex_ids=True,
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create_using=None, # return edgelist instead of Graph instance
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mg=False
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)
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)
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"""
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_ensure_args_rmat(scale, num_edges, a, b, c, seed, clip_and_flip,
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scramble_vertex_ids, create_using, mg)
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Parameters
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----------
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n_edgelists : int
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Number of edge lists (graphs) to generate
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Number of edge lists (graphs) to generate
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min_scale : int
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Scale factor to set the minimum number of vertices in the graph
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Scale factor to set the minimum number of vertices in the graph
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max_scale : int
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Scale factor to set the maximum number of vertices in the graph
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Scale factor to set the maximum number of vertices in the graph
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edge_factor : int
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Average number of edges per vertex to generate
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Average number of edges per vertex to generate
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size_distribution : int
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Distribution of the graph sizes, impacts the scale parameter of the R-MAT
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generator.
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'0' for POWER_LAW distribution and '1' for UNIFORM distribution
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Distribution of the graph sizes, impacts the scale parameter of the
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R-MAT generator.
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'0' for POWER_LAW distribution and '1' for UNIFORM distribution
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edge_distribution : int
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Edges distribution for each graph, impacts how R-MAT parameters a,b,c,d,
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are set.
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'0' for POWER_LAW distribution and '1' for UNIFORM distribution
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Edges distribution for each graph, impacts how R-MAT parameters
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a,b,c,d, are set.
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'0' for POWER_LAW distribution and '1' for UNIFORM distribution
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seed : int
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Seed value for the random number generator
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Seed value for the random number generator
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Flag controlling whether to generate edges only in the lower triangular
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part (including the diagonal) of the graph adjacency matrix
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(if set to 'true') or not (if set to 'false')
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Flag controlling whether to generate edges only in the lower triangular
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part (including the diagonal) of the graph adjacency matrix
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(if set to 'true') or not (if set to 'false')
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Flag controlling whether to scramble vertex ID bits (if set to 'true') or
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between vertex ID values and vertex degrees
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Flag controlling whether to scramble vertex ID bits (if set to 'true')
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or not (if set to 'false'); scrambling vertx ID bits breaks correlation
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between vertex ID values and vertex degrees
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Returns
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-------

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