A statistical and clustering study on Youtube 2D and 3D video recommendation graph

Ioannis Tsingalis, Ioannis Pipilis, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

2 Citations (Scopus)
520 Downloads (Pure)

Abstract

Social network sites, like Facebook, Tweeter and Flickr provide users the opportunity to share their media content, such as videos, music tracks or photos. Beyond the fact that they can share information the users can also vote or comment on information posted by other users. Social networks take advantage of this activity and create groups and communities of users with similar interests. This categorization helps social network systems to support users with data, e.g. videos, photos or users profiles, that are relevant to their interests. In order to increase the effectiveness of navigation, analysis of social media content graphs needs to be done. In this paper, an analysis of the Youtube social media graph is presented. Graphs of 2D and 3D videos are considered in this analysis. Well known properties of web and social networks analysis, like the power-law distribution are discussed. Moreover, clustering methods are applied in order to study the existence of media content groups. Finally, the results of our analysis are discussed and directions of future work are presented.
Original languageEnglish
Title of host publication2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP 2014)
Subtitle of host publicationProceedings of a meeting held 21-23 May 2014, Athens, Greece
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages294-297
Number of pages4
ISBN (Electronic)9781479928903
ISBN (Print)9781479928910
DOIs
Publication statusPublished - Nov 2014
Event6th International Symposium on Communcations, Control and Signal Processing (ISCCSP 2014) - Athens, Greece
Duration: 21 May 201423 May 2014

Conference

Conference6th International Symposium on Communcations, Control and Signal Processing (ISCCSP 2014)
CountryGreece
CityAthens
Period21/05/1423/05/14

Keywords

  • YouTube recommendation graph
  • 3D
  • social networks
  • analysis
  • clustering

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