networkx-neo4j
This library provides NetworkX API for Neo4j Graph Data Science. You should be able to use it as you would NetworkX but algorithms will run against Neo4j.
Dependencies
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≥ Neo4j 4.x
-
Graph Data Science Library Plugin
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APOC Plugin
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≥ Python 3.6
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≥ neo4j-driver 4.x
Installation
You can install the library by running the following command:
pip install networkx-neo4jYou’ll also need to install APOC and the Graph Algorithms library.
Usage
Here’s how you use it.
First let’s import our libraries and create an instance of the Neo4j driver:
>>> from neo4j import GraphDatabase
>>> import nxneo4j as nx
>>> driver = GraphDatabase.driver(uri="bolt://localhost",auth=("neo4j","your_password"))For undirected Graphs:
>>> G = nx.Graph(driver)For directed Graphs:
>>> G = nx.DiGraph(driver)The available functions in nxneo4j are:
# ADD ONE NODE
G.add_node(node)
node: str, int
>>> G.add_node(1)
# ADD MULTIPLE NODES
G.add_nodes_from(value)
values: list
>>> G.add_nodes_from([1, 2, 3, 4])
# ADD ONE EDGE
G.add_edge(node1,node2)
node1: str, int
node2: str, int
>>> G.add_edge(1,2)
#ADD MULTIPLE EDGES
G.add_edges_from(values)
values: list of tuples
>>> G.add_edges_from([(1, 2),(2, 3),(3, 4)])The available algoritms in nxneo4j are:
>>> nx.betweenness_centrality(G)
{3: 4.0, 4: 3.0, 1: 0.0, 2: 0.0, 5: 0.0}
>>> nx.closeness_centrality(G)
{3: 0.8, 4: 0.6666666666666666, 1: 0.5714285714285714, 2: 0.5714285714285714, 5: 0.4444444444444444}
>>> nx.pagerank(G)
{3: 1.4170146573314513, 4: 1.0629939728840803, 1: 0.9591085771210682, 2: 0.9591085771210682, 5: 0.6017724112363687}
>>> nx.triangles(G)
{1: 1, 2: 1, 3: 1, 4: 0, 5: 0}
>>> nx.clustering(G)
{1: 1.0, 2: 1.0, 3: 0.3333333333333333, 4: 0.0, 5: 0.0}
>>> list(nx.community.label_propagation_communities(G))
[{1, 2, 3, 4, 5}]
>>> nx.shortest_path(G, source=1, target=5)
[1, 3, 4, 5]Credits
Yusuf Baktir Mark Needham David Jablonski