This algorithm assesses how similar are 2 input partitions of a given network.
npm install normalized-mutual-informationGiven 2 partitions of the same network, NMI allows us to check how correlated are both.
This value varies between 0 (disparate partitioning) and 1 (same clusters were identified).
Install package using NPM.
``cmd`
npm i --save normalized-mutual-information
Require it using Node.js.
`node`
const nmi = require('normalized-mutual-information');
Run it!
`node
let node2com = nmi.jNMI(array1, array2);
// Each value of the array is the community to which each node (given by the position in the array) belongs.
``
#### Community Finding with Applications on Phylogenetic Networks (Master Thesis)
[Louvain](), Infomap, Layered Label Propagation,
Label Propagation, [Hamming Distance](), Girvan-Newman Benchmark
and Normalized Mutual Information algorithms were developed in JavaScript. To visualize the results, an interface
using D3.js (SVG and Canvas) and Cytoscape was implemented. Every community finding algorithm was tested in terms of accuracy, speed and memory against 2 synthetic networks (Girvan-Newman
and Lacichinetti-Fortunato-Radicchi networks with varying parameters). Final goal was to cluster microbiological data.
Check out more in the thesis website. You may also download an image of the application in Docker Hub. A description video is below.
#### Supervision Team
Alexandre Francisco (INESC-ID & IST) | João Carriço (iMM) | Vítor Borges (INSA)