Clustering, visualizing, and navigating for large dynamic graphs

Arnaud Sallaberry, Chris Muelder, Kwan-Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

In this paper, we present a new approach to exploring dynamic graphs. We have developed a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to define two visual representations. The first view is an overview that shows how clusters evolve over time and provides an interface to find and select interesting time-steps. The second view consists of a node link diagram of a selected time-step which uses the clustering to efficiently define the layout. By using the time-dependant clustering, we ensure the stability of our visualization and preserve user mental map by minimizing node motion, while simultaneously producing an ideal layout for each time step. Also, as the clustering is computed ahead of time, the second view updates in linear time which allows for interactivity even for graphs with upwards of tens of thousands of nodes.

Original languageEnglish (US)
Title of host publicationGraph Drawing - 20th International Symposium, GD 2012, Revised Selected Papers
Pages487-498
Number of pages12
DOIs
StatePublished - Feb 26 2013
Event20th International Symposium on Graph Drawing, GD 2012 - Redmond, WA, United States
Duration: Sep 19 2012Sep 21 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7704 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Symposium on Graph Drawing, GD 2012
CountryUnited States
CityRedmond, WA
Period9/19/129/21/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Clustering, visualizing, and navigating for large dynamic graphs'. Together they form a unique fingerprint.

  • Cite this

    Sallaberry, A., Muelder, C., & Ma, K-L. (2013). Clustering, visualizing, and navigating for large dynamic graphs. In Graph Drawing - 20th International Symposium, GD 2012, Revised Selected Papers (pp. 487-498). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7704 LNCS). https://doi.org/10.1007/978-3-642-36763-2_43