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This algorithm generates a Girvan-Newman synthetic network based on the input of 2 value: mix parameter and average degree. This way, 4 different ground-truth communities are generated with 32 nodes each. Each node will present a number of connections equal to the average degree. Moreover, the fraction of links between itself and nodes outside of the community it belongs will be equal to the mix parameter.
Girvan-Newman Network | mix = 0.1 | avg_deg = 16
Install package using NPM.
npm i --save girvan-newman-benchmark
Require it using Node.js.
const gn = require('girvan-newman-benchmark');
let node2com = gn.jGirvan_Newman(mix, cyto, deg); // "mix" is the fraction of links connected to any node going outwards the group it belongs to. // "cyto" is a boolean value that should be set depending on the format we want the algorithm to return. // "deg" is the degree of every node.
Louvain, Infomap, Layered Label Propagation,
Label Propagation, Hamming Distance, Girvan-Newman Benchmark
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.
I am deeply grateful for their help along this unique journey...