neo4j-graph-analytics / networkx-neo4j

NetworkX API for Neo4j Graph Algorithms.

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

• ≥ Neo4j 4.x

• Graph Data Science Library Plugin

• APOC Plugin

• ≥ Python 3.6

• ≥ neo4j-driver 4.x

Installation

You can install the library by running the following command:

pip install networkx-neo4j

You’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