Vizualizace komunit v rozsáhlých komplexních sítích s ohledem na jejich dynamický vývoj

Abstract

This master’s thesis focuses on the visualization of communities in large-scale complex networks, with an emphasis on their dynamic evolution. The goal is to design a method for presenting a graph constructed from OpenWebSearch.eu web index data in a way that remains clear and comprehensible even at the scale of millions of nodes. The work compares various community detection techniques, such as the Louvain algorithm and topic-based classification, as well as different layout strategies like embeddings with dimensionality reduction and force-directed layouts. The solution also includes testing the scalability of the proposed approach.