2026-08-08T00:00:27Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:21343 2024-10-28T09:51:06Z ddc:004 open_access

CentralityViz: Comprehending Node-Centrality in Large Networks (Software Abstract) Jindal, Garima Karlapalem, Kamalakar Visual Analytics Graph Drawing Community Detection Node Centrality CentralityVis is a software tool designed for visualizing large graphs using two community-centric methods: spiral visualization and linear visualization. Both visualizations are highly scalable, capable of handling networks with hundreds of thousands of nodes and edges. The tool leverages community detection algorithms to group nodes into communities and then orders the nodes of community on centrality in descending order, arranging them in either a spiral or linear layout. CentralityVis provides clear insights into both node and community properties, facilitating the analysis of complex networks. Each visualization method has its strengths: spiral visualization is intuitive and resembles traditional node-link diagrams, while linear visualization facilitates easy comparison of communities and offers greater scalability in terms of the number of communities that can be represented. To minimize visual clutter, edges are drawn only when needed, ensuring that even large graphs remain clear and comprehensible. CentralityVis is a powerful tool for understanding complex networks, emphasizing both individual nodes and the communities to which they belong. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Garima Jindal and Kamalakar Karlapalem 2024 Is Part Of LIPIcs, Volume 320, 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GD.2024.59 urn:nbn:de:0030-drops-213439 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2024.59 eng https://creativecommons.org/licenses/by/4.0/legalcode