A framework of filtering, clustering and dynamic layout graphs for visualization

Lai, Wei and Huang, Xiaodi and Eades, Peter (2005) A framework of filtering, clustering and dynamic layout graphs for visualization. In: ACSC 2005: 28th Australasian Computer Science Conference, Jan 2005, Newcastle, Australia.

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Many classical graph visualization algorithms have already been developed over the past decades. However, these algorithms face difficulties in practice, such as the overlapping node problem, large graph layout and dynamic graph layout. In order to solve these problems, this paper aims to systematically address algorithmic issues related to a novel framework that describes the process of graph visualization applications. First of all, a framework for graph visualization is described. As the important parts of this framework, we then develop two effective algorithms for filtering and clustering large graphs for the layouts. As for the dynamic graph layout, a new approach to removing overlapping nodes called force-transfer algorithm is developed. The framework has been implemented in a prototype called PGA to demonstrate the performance of the proposed algorithms. Finally, a case study is provided.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright ©2005, Australian Computer Society, Inc. This paper appeared at the 28th Australasian Computer Science Conference, The University of Newcastle, Australia. Conferences in Research and Practice in Information Technology, Vol. 38. V. Estivill-Castro, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is included.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 30 Nov 2007 11:55
Last Modified: 23 May 2018 05:45
Uncontrolled Keywords: algorithms; nodes; layout; graphs; information visualization; graph visualization; graph drawing; framework; filtering; clustering
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080603 Conceptual Modelling
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
URI: http://eprints.usq.edu.au/id/eprint/11748

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