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Alternate views of graph clusterings based on thresholds: a case study for a student forum

Published: 02 November 2013 Publication History

Abstract

A network represented as a graph, can be transformed to a sparser graph, if a threshold is applied to the relationship between its objects. The threshold can be used as an upper or lower limit or define a range based on which we can exclude connections from the graph, thus resulting to different views of a graph. We examine for various values of the threshold the effect it has on the task of community detection and we propose a method to validate the results of the corresponding clusterings against the clustering of the original graph. We transform the clusterings in comparable forms and we employ four known measures for clustering validation in order to examine their resemblance. We present some preliminary experiments to evaluate the effects of a threshold on the clustering task and we outline possible usage of the different views that are produced.

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  • (2013)PIKM 2013Proceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505817(2561-2562)Online publication date: 27-Oct-2013

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cover image ACM Conferences
PIKM '13: Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
November 2013
52 pages
ISBN:9781450324229
DOI:10.1145/2513166
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2013

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Author Tags

  1. clustering validation measures
  2. community detection
  3. graph clustering
  4. social networks
  5. thresholds

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PIKM '13 Paper Acceptance Rate 6 of 13 submissions, 46%;
Overall Acceptance Rate 25 of 62 submissions, 40%

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  • (2013)PIKM 2013Proceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505817(2561-2562)Online publication date: 27-Oct-2013

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