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A semi-supervised approach to visualizing and manipulating overlapping communities

Dudas, PM and De Jongh, M and Brusilovsky, P (2013) A semi-supervised approach to visualizing and manipulating overlapping communities. In: UNSPECIFIED.

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Abstract

When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. © 2013 IEEE.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Dudas, PMpmd18@pitt.eduPMD18
De Jongh, M
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Date: 1 December 2013
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Proceedings of the International Conference on Information Visualisation
Page Range: 180 - 185
Event Type: Conference
DOI or Unique Handle: 10.1109/iv.2013.23
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9780769550497
ISSN: 1093-9547
Date Deposited: 22 Jul 2013 18:02
Last Modified: 01 Nov 2017 12:57
URI: http://d-scholarship.pitt.edu/id/eprint/19389

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