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DOI: 10.4230/LIPIcs.GD.2024.59

CentralityViz: Comprehending Node-Centrality in Large Networks (Software Abstract)

Authors: Garima Jindal and Kamalakar Karlapalem

Published in: LIPIcs, Volume 320, 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

Abstract

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.

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Garima Jindal and Kamalakar Karlapalem. CentralityViz: Comprehending Node-Centrality in Large Networks (Software Abstract). In 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 320, pp. 59:1-59:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{jindal_et_al:LIPIcs.GD.2024.59,
  author =	{Jindal, Garima and Karlapalem, Kamalakar},
  title =	{{CentralityViz: Comprehending Node-Centrality in Large Networks}},
  booktitle =	{32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)},
  pages =	{59:1--59:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-343-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{320},
  editor =	{Felsner, Stefan and Klein, Karsten},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2024.59},
  URN =		{urn:nbn:de:0030-drops-213439},
  doi =		{10.4230/LIPIcs.GD.2024.59},
  annote =	{Keywords: Visual Analytics, Graph Drawing, Community Detection, Node Centrality}
}

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CentralityViz: Comprehending Node-Centrality in Large Networks (Software Abstract)

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