mscthesis.herokuapp.com

The aim of the thesis (extended abstract) was to implement three community finding algorithms – Louvain, Infomap and Layered Label Propagation; to benchmark them using two synthetic networks – Girvan-Newman and Lancichinetti-Fortunato-Radicchi; to test them in real networks, particularly, in one derived from a Staphylococcus aureus MLST dataset; to compare visualization frameworks – Cytoscape.js and D3.js (using SVG and Canvas elements), and, finally, to make it all available online.

• Louvain;
• Infomap;
• Layered Label Propagation (LLP);
• Girvan-Newman (GN) Benchmark Network Generator;
• Normalized Mutual Information (NMI);
• Hamming Distance.

• Girvan-Newman;
• Lancichinetti-Fortunato-Radicchi (LFR).

• Amazon;
• Zachary's Karate Club;
• Staphylococcus aureus MLST Dataset.

Congruence of each partition inferred by Louvain, Infomap and LLP was determined using NMI.

Time required to run Louvain, Infomap and LLP, in GN and LFR networks, was measured. As well as, the time needed to execute GN Benchmark Network Generator, considering different mixing and average node degree parameters.

• Cytoscape.js;
• D3.js (Version 4 & SVG);
• D3.js (Version 4 & Canvas).

Web application which integrates all the previous components. Image available in Docker Hub. Description video below.

Alexandre Francisco (INESC-ID & IST) | João Carriço (iMM & IST) | Vítor Borges (INSA)

Roadmap -> Wiki