Adaptive visualization of research communities

Martijn De Jongh, Patrick M. Dudas, Peter Brusilovsky

Research output: Contribution to journalConference articlepeer-review


Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
StatePublished - 2013
Event21st Conference on User Modeling, Adaptation, and Personalization, UMAP 2013 - Rome, Italy
Duration: Jun 10 2013Jun 14 2013

All Science Journal Classification (ASJC) codes

  • General Computer Science


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