Official Python API for NOESIS, an open source framework for network data mining that provides a large collection of network analysis techniques, including the analysis of network structural properties, community detection methods, link scoring, and link prediction, as well as network visualization algorithms.

From PyPi:

pip install noesis

From source:

git clone https://github.com/fvictor/noesis-python.git
cd noesis-python
python setup.py install

NOESIS for Python provides simple and unified interfaces for most of the implemented techniques. The following example loads a network from a GML file and detects its communities using the Kernighan–Lin algorithm:

from noesis import Noesis

ns = Noesis()

network_reader = ns.create_network_reader('GML')
network = network_reader.read('my_network.gml')

community_detector = ns.create_community_detector('KernighanLin')
communities = community_detector.compute(network)

for node in range(network.nodes()):
	print('Node {} belongs to community {}'.format(node, communities[node]))

ns.end()

The following example generates a network of 20 nodes and 100 links using the Erdös–Rényi model and computes the PageRank score of each node:

from noesis import Noesis

ns = Noesis()

network = ns.create_network_from_model('ErdosRenyi', 20, 100)

pagerank_scorer = ns.create_node_scorer('PageRank', 0.9)
scores = pagerank_scorer.compute(network)

for node in range(network.nodes()):
	print('Node {} has a PageRank score of {}'.format(node, scores[node]))

ns.end()

Always remember to call the end method of Noesis class to properly terminate the NOESIS session and finish the execution of your program.

The documentation of the project, which can be built using SPHINX, is located in the doc folder. A readily available up-to-date version of the documentation can be found at http://noesis-python.readthedocs.io.