nx-parallel

nx-parallel is a NetworkX backend that uses joblib for parallelization. This project aims to provide parallelized implementations of various NetworkX functions to improve performance.

Algorithms in nx-parallel

• number_of_isolates
• square_clustering
• local_efficiency
• closeness_vitality
• is_reachable
• tournament_is_strongly_connected
• all_pairs_node_connectivity
• approximate_all_pairs_node_connectivity
• betweenness_centrality
• node_redundancy
• all_pairs_dijkstra
• all_pairs_dijkstra_path_length
• all_pairs_dijkstra_path
• all_pairs_bellman_ford_path_length
• all_pairs_bellman_ford_path
• johnson
• all_pairs_all_shortest_paths
• all_pairs_shortest_path_length
• all_pairs_shortest_path

Script used to generate the above list

import _nx_parallel as nxp
d = nxp.get_funcs_info() # temporarily add `from .update_get_info import *` to _nx_parallel/__init__.py
for func in d:
    print(f"- [{func}]({d[func]['url']})")

Backend usage

import networkx as nx
import nx_parallel as nxp

G = nx.path_graph(4)
H = nxp.ParallelGraph(G)

# method 1 : passing ParallelGraph object in networkx function
nx.betweenness_centrality(H)

# method 2 : using the 'backend' kwarg
nx.betweenness_centrality(G, backend="parallel")

# method 3 : using nx-parallel implementation with networkx object
nxp.betweenness_centrality(G)

# method 4 : using nx-parallel implementation with ParallelGraph object
nxp.betweenness_centrality(H)

# output : {0: 0.0, 1: 0.6666666666666666, 2: 0.6666666666666666, 3: 0.0}

Notes

1. Some functions in networkx have the same name but different implementations, so to avoid these name conflicts at the time of dispatching networkx differentiates them by specifying the name parameter in the _dispatchable decorator of such algorithms. So, method 3 and method 4 are not recommended. But, you can use them if you know the correct name. For example:

   # using `name` parameter - nx-parallel as an independent package
   nxp.all_pairs_node_connectivity(H) # runs the parallel implementation in `connectivity/connectivity`
   nxp.approximate_all_pairs_node_connectivity(H) # runs the parallel implementation in `approximation/connectivity`

   Also, if you are using nx-parallel as a backend then mentioning the subpackage to which the algorithm belongs is recommended to ensure that networkx dispatches to the correct implementation. For example:

   # with subpackage - nx-parallel as a backend
   nx.all_pairs_node_connectivity(H)
   nx.approximation.all_pairs_node_connectivity(H)

2. Right now there isn't much difference between nx.Graph and nxp.ParallelGraph so method 3 would work fine but it is not recommended because in the future that might not be the case.

Feel free to contribute to nx-parallel. You can find the contributing guidelines here. If you'd like to implement a feature or fix a bug, we'd be happy to review a pull request. Please make sure to explain the changes you made in the pull request description. And feel free to open issues for any problems you face, or for new features you'd like to see implemented.

Thank you :)