Interactive visualization of community structure in complex networks

Several applied sciences model system dynamics with networks. Since networks often contain thousands or millions of nodes and links, researchers have developed methods that reveal and high- light their essential structures. One such method developed by researchers in IceLab uses information theory to compress descrip- tions of network flows with memory based on paths rather than links and identify hierarchically nested modules with long flow persistence times. However, current visualization tools for navigat- ing and exploring nested modules build on obsolete software that requires plugins and cannot handle such memory networks. Drawing from ideas in cartography, this thesis presents a pow- erful visualization method that enables researchers to analyze and explore modular decompositions of any network. The resulting application uses an efficient graph layout algorithm adapted with a simulation based on information flow. Like in a topographic map, zooming into the map successively reveals more detailed commu- nity structures and network features in a continuous fashion