Semantically modified diffusion limited aggregation for visualizing large-scale networks

Chaomei Chen, Natasha Lobo

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

2 Citations (Scopus)


Diffusion-Limited Aggregation (DLA) is a model of fractal growth. Computer models can simulate the fast aggregation of millions of particles. In this paper, we propose a modified version of DLA, called semantically modified DLA (SM-DLA), for visualizing large-scale networks. SM-DLA introduces similarity measures between particles so that instead of attaching to the nearest particle in the aggregation, a new particle is stochastically directed to attach to particles that are similar to it. The results of our initial experiment with a co-citation network using SM-DLA are encouraging, suggesting that the algorithm has the potential as an alternative paradigm for visualizing large-scale networks. Further studies in this direction are recommended.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Information Visualization
Subtitle of host publicationAn International Conference on Computer Visualization and Graphics Applications, IV 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0769519881
Publication statusPublished - 2003
Event7th International Conference on Information Visualization, IV 2003 - London, United Kingdom
Duration: 2003 Jul 162003 Jul 18

Publication series

NameProceedings of the International Conference on Information Visualisation
ISSN (Print)1093-9547


Other7th International Conference on Information Visualization, IV 2003
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2003 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Semantically modified diffusion limited aggregation for visualizing large-scale networks'. Together they form a unique fingerprint.

Cite this