Literature DB >> 18467763

GrouseFlocks: steerable exploration of graph hierarchy space.

Daniel Archambault1, Tamara Munzner, David Auber.   

Abstract

Several previous systems allow users to interactively explore a large input graph through cuts of a superimposed hierarchy. This hierarchy is often created using clustering algorithms or topological features present in the graph. However, many graphs have domain-specific attributes associated with the nodes and edges, which could be used to create many possible hierarchies providing unique views of the input graph. GrouseFlocks is a system for the exploration of this graph hierarchy space. By allowing users to see several different possible hierarchies on the same graph, the system helps users investigate graph hierarchy space instead of a single fixed hierarchy. GrouseFlocks provides a simple set of operations so that users can create and modify their graph hierarchies based on selections. These selections can be made manually or based on patterns in the attribute data provided with the graph. It provides feedback to the user within seconds, allowing interactive exploration of this space.

Mesh:

Year:  2008        PMID: 18467763     DOI: 10.1109/TVCG.2008.34

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  3 in total

1.  Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks.

Authors:  Alan Demétrius Baria Valejo; Renato Fabbri; Alneu de Andrade Lopes; Liang Zhao; Maria Cristina Ferreira de Oliveira
Journal:  Front Res Metr Anal       Date:  2022-06-16

2.  FragViz: visualization of fragmented networks.

Authors:  Miha Stajdohar; Minca Mramor; Blaž Zupan; Janez Demšar
Journal:  BMC Bioinformatics       Date:  2010-09-22       Impact factor: 3.169

3.  MCLEAN: Multilevel Clustering Exploration As Network.

Authors:  Daniel Alcaide; Jan Aerts
Journal:  PeerJ Comput Sci       Date:  2018-01-29
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.