Readable representations for large-scale bipartite graphs

Shuji Sato*, Kazuo Misue, Jiro Tanaka

*Corresponding author for this work

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

4 Citations (Scopus)


Bipartite graphs appear in various scenes in the real world, and visualizing these graphs helps improve our understanding of network structures. The amount of information that is available to us has increased dramatically in recent years, and it is therefore necessary to develop a drawing technique that corresponds to large-scale graphs. In this paper, we describe drawing methods to make large-scale bipartite graphs easy to read. We propose two techniques: "node contraction drawing," which involves collecting similar nodes and drawing them as one node, and "isosimilarity contour drawing," which puts clusters into an outlined area. We developed interactive user interfaces for the drawing methods and conducted an evaluation experiment to demonstrate the effectiveness of the proposed techniques.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings
PublisherSpringer Verlag
Number of pages8
EditionPART 2
ISBN (Print)3540855645, 9783540855644
Publication statusPublished - 2008
Externally publishedYes
Event12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb, Croatia
Duration: 2008 Sep 32008 Sep 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5178 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008


  • Anchored map
  • Bipartite graph
  • Clustering
  • Graph drawing
  • Information visualization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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