An open source framework for network data mining that provides a large collection of network analysis techniques
Project description
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.
Installation
From source:
git clone https://github.com/fvictor/noesis-python.git
cd noesis-python
python setup.py install
From PyPi:
pip install noesis
Getting started
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.
Documentation
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.
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