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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-148551 |
| Date | January 2018 |
| Creators | Eriksson, Anton |
| Publisher | Umeå universitet, Institutionen för fysik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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