Layered Label Propagation Algorithm
Description
The update rule distinguishes Layered Label Propagation from Label Propagation. Instead of limiting the community choice to the one that is predominant among the nodes in the neighborhood, it takes into account a factor that considers the labeled nodes in the complete network. In fact, both algorithm versions are equivalent whenever this factor is considered 0.
Optimizing equation:
Usage
Install package using NPM.
npm i --save layered-label-propagationRequire it using Node.js.
const llp = require('layered-label-propagation');Start community finding.
let node2com = llp.jLayeredLabelPropagation(nodes, links, gamma, max); // node2com = {nodeID1: commmunityID1, nodeID2: commmunityID2...}// nodes = [nodeID1, nodeID2...]// links = [{source: nodeID1, target: nodeID2, value: weight ...}]// gamma // Whenever the number of iterations exceeds a given "max", algorithms stops further partitioning of the network. More
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)