With the advent of high-throughput sequencing methods, new ways of visualizing and analyzing increasingly amounts of data are needed. Although some software already exist, they do not scale well or require advanced skills to be useful in phylogenetics. The aim of this thesis was to implement three community finding algorithms -Louvain, Infomap and Layered Label Propagation (LLP); to benchmark them using two synthetic networks -Girvan-Newman (GN) and Lancichinetti-Fortunato-Radicchi (LFR); 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, and, finally, to make it all available online (mscthesis.herokuapp.com). Louvain, Infomap and LLP were implemented in JavaScript. Unless otherwise stated, next conclusions are valid for GN and LFR. In terms of speed, Louvain outperformed all others. Considering accuracy, in networks with well-defined communities, Louvain was the most accurate. For higher mixing, LLP was the best. Contrarily to weakly mixed, it is advantageous to increase the resolution parameter in highly mixed GN. In LFR, higher resolution decreases the accuracy of detection, independently of the mixing parameter. The increase of the average node degree enhanced partitioning accuracy and suggested detection by chance was minimized. It is computationally more intensive to generate GN with higher mixing or average degree, using the algorithm developed in the thesis or the LFR implementation. In S. aureus network, Louvain was the fastest and the most accurate in detecting the clusters of seven groups of strains directly evolved from the common ancestor.
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