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A meta heuristic for graph drawing: learning the optimal graph-drawing method for clustered graphs

Published:01 May 2000Publication History

ABSTRACT

The problem of finding a pleasant layout for a given graph is a key challenge in the field of information visualization. For graphs that are biased towards a particular property such as tree-like, star-like, or bipartite, a layout algorithm can produce excellent layouts—if this property is actually detected.

Typically, a graph may not be of such a homogeneous shape but is comprised of different parts, or it provides several levels of abstraction each of which dominated by another property.

The paper in hand addresses the layout of such graphs. It presents a meta heuristic for graph drawing, which is based on two ideas: (i) The detection and exploitation of hierarchical cluster information to unveil a graph's inherent structure. (ii) The automatic selection of an individual graph drawing method for each cluster.

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  1. A meta heuristic for graph drawing: learning the optimal graph-drawing method for clustered graphs

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                • Published in

                  cover image ACM Conferences
                  AVI '00: Proceedings of the working conference on Advanced visual interfaces
                  May 2000
                  317 pages
                  ISBN:1581132522
                  DOI:10.1145/345513

                  Copyright © 2000 ACM

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                  Association for Computing Machinery

                  New York, NY, United States

                  Publication History

                  • Published: 1 May 2000

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                  Overall Acceptance Rate107of408submissions,26%