| Name | graphblas-algorithms JSON |
| Version |
2023.10.0
JSON |
| download |
| home_page | |
| Summary | Graph algorithms written in GraphBLAS and backend for NetworkX |
| upload_time | 2023-10-19 02:57:18 |
| maintainer | |
| docs_url | None |
| author | Jim Kitchen, Graphblas-algorithms contributors |
| requires_python | >=3.9 |
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| keywords |
graphblas
graph
sparse
matrix
lagraph
suitesparse
networks
graph theory
mathematics
network
discrete mathematics
math
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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<br>
[](https://anaconda.org/conda-forge/graphblas-algorithms)
[](https://pypi.python.org/pypi/graphblas-algorithms/)
[](https://pypi.python.org/pypi/graphblas-algorithms/)
[](https://github.com/python-graphblas/graphblas-algorithms/blob/main/LICENSE)
<br>
[](https://github.com/python-graphblas/graphblas-algorithms/actions)
[](https://codecov.io/gh/python-graphblas/graphblas-algorithms)
[](https://doi.org/10.5281/zenodo.7329185)
[](https://discord.com/invite/vur45CbwMz)
<!--- [](https://graphblas-algorithms.readthedocs.io/en/latest/) --->
`graphblas-algorithms` is a collection of GraphBLAS algorithms written using
[`python-graphblas`](https://python-graphblas.readthedocs.io/en/latest/).
It may be used directly or as an experimental
[backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends).
Why use GraphBLAS Algorithms? Because it is *fast*, *flexible*, and *familiar* by using the NetworkX API.
Are we missing any [algorithms](#Plugin-Algorithms) that you want?
[Please let us know!](https://github.com/python-graphblas/graphblas-algorithms/issues)
<br>
<img src="https://raw.githubusercontent.com/python-graphblas/graphblas-algorithms/main/docs/_static/img/graphblas-vs-networkx.png" alt="GraphBLAS vs NetworkX" title="Even faster than scipy.sparse!" width="640" />
<br>
<img src="https://raw.githubusercontent.com/python-graphblas/graphblas-algorithms/main/docs/_static/img/graphblas-vs-igraph.png" alt="GraphBLAS vs igraph" title="igraph may use different algorithms for PageRank" width="600" />
### Installation
```
conda install -c conda-forge graphblas-algorithms
```
```
pip install graphblas-algorithms
```
## Basic Usage
First, create a GraphBLAS Matrix.
```python
import graphblas as gb
M = gb.Matrix.from_coo(
[0, 0, 1, 2, 2, 3],
[1, 3, 0, 0, 1, 2],
[1., 2., 3., 4., 5., 6.],
nrows=4, ncols=4, dtype='float32'
)
```
Next wrap the Matrix as `ga.Graph`.
```python
import graphblas_algorithms as ga
G = ga.Graph(M)
```
Finally call an algorithm.
```python
hubs, authorities = ga.hits(G)
```
When the result is a value per node, a `gb.Vector` will be returned.
In the case of [HITS](https://en.wikipedia.org/wiki/HITS_algorithm),
two Vectors are returned representing the hubs and authorities values.
Algorithms whose result is a subgraph will return `ga.Graph`.
## Plugin for NetworkX
Dispatching to plugins is a new feature in Networkx 3.0.
When both `networkx` and `graphblas-algorithms` are installed in an
environment, calls to NetworkX algorithms can be dispatched to the
equivalent version in `graphblas-algorithms`.
### Dispatch Example
```python
import networkx as nx
import graphblas_algorithms as ga
# Generate a random graph (5000 nodes, 1_000_000 edges)
G = nx.erdos_renyi_graph(5000, 0.08)
# Explicitly convert to ga.Graph
G2 = ga.Graph.from_networkx(G)
# Pass G2 to NetworkX's k_truss
T5 = nx.k_truss(G2, 5)
```
`G2` is not a `nx.Graph`, but it does have an attribute
`__networkx_plugin__ = "graphblas"`. This tells NetworkX to
dispatch the k_truss call to graphblas-algorithms. This link
connection exists because graphblas-algorithms registers
itself as a "networkx.plugin" entry point.
The result `T5` is a `ga.Graph` representing the 5-truss structure of the
original graph. To convert to a NetworkX Graph, use:
```python
T5.to_networkx()
```
Note that even with the conversions to and from `ga.Graph`, this example still runs 10x
faster than using the native NetworkX k-truss implementation. Speed improvements scale
with graph size, so larger graphs will see an even larger speed-up relative to NetworkX.
### Plugin Algorithms
The following NetworkX algorithms have been implemented
by graphblas-algorithms and can be used following the
dispatch pattern shown above.
[//]: # (Begin auto-generated code)
```
graphblas_algorithms.nxapi
├── boundary
│ ├── edge_boundary
│ └── node_boundary
├── centrality
│ ├── degree_alg
│ │ ├── degree_centrality
│ │ ├── in_degree_centrality
│ │ └── out_degree_centrality
│ ├── eigenvector
│ │ └── eigenvector_centrality
│ └── katz
│ └── katz_centrality
├── cluster
│ ├── average_clustering
│ ├── clustering
│ ├── generalized_degree
│ ├── square_clustering
│ ├── transitivity
│ └── triangles
├── community
│ └── quality
│ ├── inter_community_edges
│ └── intra_community_edges
├── components
│ ├── connected
│ │ ├── is_connected
│ │ └── node_connected_component
│ └── weakly_connected
│ └── is_weakly_connected
├── core
│ └── k_truss
├── cuts
│ ├── boundary_expansion
│ ├── conductance
│ ├── cut_size
│ ├── edge_expansion
│ ├── mixing_expansion
│ ├── node_expansion
│ ├── normalized_cut_size
│ └── volume
├── dag
│ ├── ancestors
│ └── descendants
├── dominating
│ └── is_dominating_set
├── efficiency_measures
│ └── efficiency
├── generators
│ └── ego
│ └── ego_graph
├── isolate
│ ├── is_isolate
│ ├── isolates
│ └── number_of_isolates
├── isomorphism
│ └── isomorph
│ ├── fast_could_be_isomorphic
│ └── faster_could_be_isomorphic
├── linalg
│ ├── bethehessianmatrix
│ │ └── bethe_hessian_matrix
│ ├── graphmatrix
│ │ └── adjacency_matrix
│ ├── laplacianmatrix
│ │ ├── laplacian_matrix
│ │ └── normalized_laplacian_matrix
│ └── modularitymatrix
│ ├── directed_modularity_matrix
│ └── modularity_matrix
├── link_analysis
│ ├── hits_alg
│ │ └── hits
│ └── pagerank_alg
│ ├── google_matrix
│ └── pagerank
├── lowest_common_ancestors
│ └── lowest_common_ancestor
├── operators
│ ├── binary
│ │ ├── compose
│ │ ├── difference
│ │ ├── disjoint_union
│ │ ├── full_join
│ │ ├── intersection
│ │ ├── symmetric_difference
│ │ └── union
│ └── unary
│ ├── complement
│ └── reverse
├── reciprocity
│ ├── overall_reciprocity
│ └── reciprocity
├── regular
│ ├── is_k_regular
│ └── is_regular
├── shortest_paths
│ ├── dense
│ │ ├── floyd_warshall
│ │ ├── floyd_warshall_numpy
│ │ └── floyd_warshall_predecessor_and_distance
│ ├── generic
│ │ └── has_path
│ ├── unweighted
│ │ ├── all_pairs_shortest_path_length
│ │ ├── single_source_shortest_path_length
│ │ └── single_target_shortest_path_length
│ └── weighted
│ ├── all_pairs_bellman_ford_path_length
│ ├── bellman_ford_path
│ ├── bellman_ford_path_length
│ ├── negative_edge_cycle
│ └── single_source_bellman_ford_path_length
├── simple_paths
│ └── is_simple_path
├── smetric
│ └── s_metric
├── structuralholes
│ └── mutual_weight
├── tournament
│ ├── is_tournament
│ ├── score_sequence
│ └── tournament_matrix
├── traversal
│ └── breadth_first_search
│ ├── bfs_layers
│ └── descendants_at_distance
└── triads
└── is_triad
```
[//]: # (End auto-generated code)
Raw data
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"name": "graphblas-algorithms",
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"requires_python": ">=3.9",
"maintainer_email": "Erik Welch <erik.n.welch@gmail.com>, Jim Kitchen <jim22k@gmail.com>",
"keywords": "graphblas,graph,sparse,matrix,lagraph,suitesparse,Networks,Graph Theory,Mathematics,network,discrete mathematics,math",
"author": "Jim Kitchen, Graphblas-algorithms contributors",
"author_email": "Erik Welch <erik.n.welch@gmail.com>",
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"description": "\n<br>\n[](https://anaconda.org/conda-forge/graphblas-algorithms)\n[](https://pypi.python.org/pypi/graphblas-algorithms/)\n[](https://pypi.python.org/pypi/graphblas-algorithms/)\n[](https://github.com/python-graphblas/graphblas-algorithms/blob/main/LICENSE)\n<br>\n[](https://github.com/python-graphblas/graphblas-algorithms/actions)\n[](https://codecov.io/gh/python-graphblas/graphblas-algorithms)\n[](https://doi.org/10.5281/zenodo.7329185)\n[](https://discord.com/invite/vur45CbwMz)\n<!--- [](https://graphblas-algorithms.readthedocs.io/en/latest/) --->\n\n`graphblas-algorithms` is a collection of GraphBLAS algorithms written using\n[`python-graphblas`](https://python-graphblas.readthedocs.io/en/latest/).\nIt may be used directly or as an experimental\n[backend to NetworkX](https://networkx.org/documentation/stable/reference/classes/index.html#backends).\n\nWhy use GraphBLAS Algorithms? Because it is *fast*, *flexible*, and *familiar* by using the NetworkX API.\n\nAre we missing any [algorithms](#Plugin-Algorithms) that you want?\n[Please let us know!](https://github.com/python-graphblas/graphblas-algorithms/issues)\n<br>\n<img src=\"https://raw.githubusercontent.com/python-graphblas/graphblas-algorithms/main/docs/_static/img/graphblas-vs-networkx.png\" alt=\"GraphBLAS vs NetworkX\" title=\"Even faster than scipy.sparse!\" width=\"640\" />\n<br>\n<img src=\"https://raw.githubusercontent.com/python-graphblas/graphblas-algorithms/main/docs/_static/img/graphblas-vs-igraph.png\" alt=\"GraphBLAS vs igraph\" title=\"igraph may use different algorithms for PageRank\" width=\"600\" />\n\n### Installation\n```\nconda install -c conda-forge graphblas-algorithms\n```\n```\npip install graphblas-algorithms\n```\n\n## Basic Usage\n\nFirst, create a GraphBLAS Matrix.\n\n```python\nimport graphblas as gb\n\nM = gb.Matrix.from_coo(\n [0, 0, 1, 2, 2, 3],\n [1, 3, 0, 0, 1, 2],\n [1., 2., 3., 4., 5., 6.],\n nrows=4, ncols=4, dtype='float32'\n)\n```\n\nNext wrap the Matrix as `ga.Graph`.\n\n```python\nimport graphblas_algorithms as ga\n\nG = ga.Graph(M)\n```\n\nFinally call an algorithm.\n\n```python\nhubs, authorities = ga.hits(G)\n```\n\nWhen the result is a value per node, a `gb.Vector` will be returned.\nIn the case of [HITS](https://en.wikipedia.org/wiki/HITS_algorithm),\ntwo Vectors are returned representing the hubs and authorities values.\n\nAlgorithms whose result is a subgraph will return `ga.Graph`.\n\n## Plugin for NetworkX\n\nDispatching to plugins is a new feature in Networkx 3.0.\nWhen both `networkx` and `graphblas-algorithms` are installed in an\nenvironment, calls to NetworkX algorithms can be dispatched to the\nequivalent version in `graphblas-algorithms`.\n\n### Dispatch Example\n```python\nimport networkx as nx\nimport graphblas_algorithms as ga\n\n# Generate a random graph (5000 nodes, 1_000_000 edges)\nG = nx.erdos_renyi_graph(5000, 0.08)\n\n# Explicitly convert to ga.Graph\nG2 = ga.Graph.from_networkx(G)\n\n# Pass G2 to NetworkX's k_truss\nT5 = nx.k_truss(G2, 5)\n```\n\n`G2` is not a `nx.Graph`, but it does have an attribute\n`__networkx_plugin__ = \"graphblas\"`. This tells NetworkX to\ndispatch the k_truss call to graphblas-algorithms. This link\nconnection exists because graphblas-algorithms registers\nitself as a \"networkx.plugin\" entry point.\n\nThe result `T5` is a `ga.Graph` representing the 5-truss structure of the\noriginal graph. To convert to a NetworkX Graph, use:\n```python\nT5.to_networkx()\n```\n\nNote that even with the conversions to and from `ga.Graph`, this example still runs 10x\nfaster than using the native NetworkX k-truss implementation. Speed improvements scale\nwith graph size, so larger graphs will see an even larger speed-up relative to NetworkX.\n\n### Plugin Algorithms\n\nThe following NetworkX algorithms have been implemented\nby graphblas-algorithms and can be used following the\ndispatch pattern shown above.\n\n[//]: # (Begin auto-generated code)\n\n```\ngraphblas_algorithms.nxapi\n\u251c\u2500\u2500 boundary\n\u2502 \u251c\u2500\u2500 edge_boundary\n\u2502 \u2514\u2500\u2500 node_boundary\n\u251c\u2500\u2500 centrality\n\u2502 \u251c\u2500\u2500 degree_alg\n\u2502 \u2502 \u251c\u2500\u2500 degree_centrality\n\u2502 \u2502 \u251c\u2500\u2500 in_degree_centrality\n\u2502 \u2502 \u2514\u2500\u2500 out_degree_centrality\n\u2502 \u251c\u2500\u2500 eigenvector\n\u2502 \u2502 \u2514\u2500\u2500 eigenvector_centrality\n\u2502 \u2514\u2500\u2500 katz\n\u2502 \u2514\u2500\u2500 katz_centrality\n\u251c\u2500\u2500 cluster\n\u2502 \u251c\u2500\u2500 average_clustering\n\u2502 \u251c\u2500\u2500 clustering\n\u2502 \u251c\u2500\u2500 generalized_degree\n\u2502 \u251c\u2500\u2500 square_clustering\n\u2502 \u251c\u2500\u2500 transitivity\n\u2502 \u2514\u2500\u2500 triangles\n\u251c\u2500\u2500 community\n\u2502 \u2514\u2500\u2500 quality\n\u2502 \u251c\u2500\u2500 inter_community_edges\n\u2502 \u2514\u2500\u2500 intra_community_edges\n\u251c\u2500\u2500 components\n\u2502 \u251c\u2500\u2500 connected\n\u2502 \u2502 \u251c\u2500\u2500 is_connected\n\u2502 \u2502 \u2514\u2500\u2500 node_connected_component\n\u2502 \u2514\u2500\u2500 weakly_connected\n\u2502 \u2514\u2500\u2500 is_weakly_connected\n\u251c\u2500\u2500 core\n\u2502 \u2514\u2500\u2500 k_truss\n\u251c\u2500\u2500 cuts\n\u2502 \u251c\u2500\u2500 boundary_expansion\n\u2502 \u251c\u2500\u2500 conductance\n\u2502 \u251c\u2500\u2500 cut_size\n\u2502 \u251c\u2500\u2500 edge_expansion\n\u2502 \u251c\u2500\u2500 mixing_expansion\n\u2502 \u251c\u2500\u2500 node_expansion\n\u2502 \u251c\u2500\u2500 normalized_cut_size\n\u2502 \u2514\u2500\u2500 volume\n\u251c\u2500\u2500 dag\n\u2502 \u251c\u2500\u2500 ancestors\n\u2502 \u2514\u2500\u2500 descendants\n\u251c\u2500\u2500 dominating\n\u2502 \u2514\u2500\u2500 is_dominating_set\n\u251c\u2500\u2500 efficiency_measures\n\u2502 \u2514\u2500\u2500 efficiency\n\u251c\u2500\u2500 generators\n\u2502 \u2514\u2500\u2500 ego\n\u2502 \u2514\u2500\u2500 ego_graph\n\u251c\u2500\u2500 isolate\n\u2502 \u251c\u2500\u2500 is_isolate\n\u2502 \u251c\u2500\u2500 isolates\n\u2502 \u2514\u2500\u2500 number_of_isolates\n\u251c\u2500\u2500 isomorphism\n\u2502 \u2514\u2500\u2500 isomorph\n\u2502 \u251c\u2500\u2500 fast_could_be_isomorphic\n\u2502 \u2514\u2500\u2500 faster_could_be_isomorphic\n\u251c\u2500\u2500 linalg\n\u2502 \u251c\u2500\u2500 bethehessianmatrix\n\u2502 \u2502 \u2514\u2500\u2500 bethe_hessian_matrix\n\u2502 \u251c\u2500\u2500 graphmatrix\n\u2502 \u2502 \u2514\u2500\u2500 adjacency_matrix\n\u2502 \u251c\u2500\u2500 laplacianmatrix\n\u2502 \u2502 \u251c\u2500\u2500 laplacian_matrix\n\u2502 \u2502 \u2514\u2500\u2500 normalized_laplacian_matrix\n\u2502 \u2514\u2500\u2500 modularitymatrix\n\u2502 \u251c\u2500\u2500 directed_modularity_matrix\n\u2502 \u2514\u2500\u2500 modularity_matrix\n\u251c\u2500\u2500 link_analysis\n\u2502 \u251c\u2500\u2500 hits_alg\n\u2502 \u2502 \u2514\u2500\u2500 hits\n\u2502 \u2514\u2500\u2500 pagerank_alg\n\u2502 \u251c\u2500\u2500 google_matrix\n\u2502 \u2514\u2500\u2500 pagerank\n\u251c\u2500\u2500 lowest_common_ancestors\n\u2502 \u2514\u2500\u2500 lowest_common_ancestor\n\u251c\u2500\u2500 operators\n\u2502 \u251c\u2500\u2500 binary\n\u2502 \u2502 \u251c\u2500\u2500 compose\n\u2502 \u2502 \u251c\u2500\u2500 difference\n\u2502 \u2502 \u251c\u2500\u2500 disjoint_union\n\u2502 \u2502 \u251c\u2500\u2500 full_join\n\u2502 \u2502 \u251c\u2500\u2500 intersection\n\u2502 \u2502 \u251c\u2500\u2500 symmetric_difference\n\u2502 \u2502 \u2514\u2500\u2500 union\n\u2502 \u2514\u2500\u2500 unary\n\u2502 \u251c\u2500\u2500 complement\n\u2502 \u2514\u2500\u2500 reverse\n\u251c\u2500\u2500 reciprocity\n\u2502 \u251c\u2500\u2500 overall_reciprocity\n\u2502 \u2514\u2500\u2500 reciprocity\n\u251c\u2500\u2500 regular\n\u2502 \u251c\u2500\u2500 is_k_regular\n\u2502 \u2514\u2500\u2500 is_regular\n\u251c\u2500\u2500 shortest_paths\n\u2502 \u251c\u2500\u2500 dense\n\u2502 \u2502 \u251c\u2500\u2500 floyd_warshall\n\u2502 \u2502 \u251c\u2500\u2500 floyd_warshall_numpy\n\u2502 \u2502 \u2514\u2500\u2500 floyd_warshall_predecessor_and_distance\n\u2502 \u251c\u2500\u2500 generic\n\u2502 \u2502 \u2514\u2500\u2500 has_path\n\u2502 \u251c\u2500\u2500 unweighted\n\u2502 \u2502 \u251c\u2500\u2500 all_pairs_shortest_path_length\n\u2502 \u2502 \u251c\u2500\u2500 single_source_shortest_path_length\n\u2502 \u2502 \u2514\u2500\u2500 single_target_shortest_path_length\n\u2502 \u2514\u2500\u2500 weighted\n\u2502 \u251c\u2500\u2500 all_pairs_bellman_ford_path_length\n\u2502 \u251c\u2500\u2500 bellman_ford_path\n\u2502 \u251c\u2500\u2500 bellman_ford_path_length\n\u2502 \u251c\u2500\u2500 negative_edge_cycle\n\u2502 \u2514\u2500\u2500 single_source_bellman_ford_path_length\n\u251c\u2500\u2500 simple_paths\n\u2502 \u2514\u2500\u2500 is_simple_path\n\u251c\u2500\u2500 smetric\n\u2502 \u2514\u2500\u2500 s_metric\n\u251c\u2500\u2500 structuralholes\n\u2502 \u2514\u2500\u2500 mutual_weight\n\u251c\u2500\u2500 tournament\n\u2502 \u251c\u2500\u2500 is_tournament\n\u2502 \u251c\u2500\u2500 score_sequence\n\u2502 \u2514\u2500\u2500 tournament_matrix\n\u251c\u2500\u2500 traversal\n\u2502 \u2514\u2500\u2500 breadth_first_search\n\u2502 \u251c\u2500\u2500 bfs_layers\n\u2502 \u2514\u2500\u2500 descendants_at_distance\n\u2514\u2500\u2500 triads\n \u2514\u2500\u2500 is_triad\n```\n\n[//]: # (End auto-generated code)\n",
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