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Visit me, click me, be my friend: an analysis of evidence networks of user relationships in BibSonomy

Published: 13 June 2010 Publication History

Abstract

The ongoing spread of online social networking and sharing sites has reshaped the way how people interact with each other. Analyzing the relatedness of different users within the resulting large populations of these systems plays an important role for tasks like user recommendation or community detection. Algorithms in these fields typically face the problem that explicit user relationships (like friend lists) are often very sparse. Surprisingly, implicit evidences (like click logs) of user relations have hardly been considered to this end.
Based on our long-time experience with running BibSonomy [4], we identify in this paper different evidence networks of user relationships in our system. We broadly classify each network based on whether the links are explicitly established by the users (e.g., friendship or group membership) or accrue implicitly in the running system (e.g., when user u copies an entry of user v). We systematically analyze structural properties of these networks and whether topological closeness (in terms of the length of shortest paths) coincides with semantic similarity between users.
Our results exhibit different characteristics and. provide preparatory work for the inclusion of new (and less sparse) information into the process of optimizing community detection or user recommendation algorithms.

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      cover image ACM Conferences
      HT '10: Proceedings of the 21st ACM conference on Hypertext and hypermedia
      June 2010
      328 pages
      ISBN:9781450300414
      DOI:10.1145/1810617
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      Published: 13 June 2010

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      Author Tags

      1. community detection
      2. folksonomies
      3. social networks
      4. user recommendation

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      HT '10: 21st ACM Conference on Hypertext and Hypermedia
      June 13 - 16, 2010
      Ontario, Toronto, Canada

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      Cited By

      View all
      • (2021)Semantic Data Mining in Ubiquitous Sensing: A SurveySensors10.3390/s2113432221:13(4322)Online publication date: 24-Jun-2021
      • (2017)A heuristic approach to discovering user correlations from organized social stream dataMultimedia Tools and Applications10.1007/s11042-014-2153-576:9(11487-11507)Online publication date: 1-May-2017
      • (2014)The social distributional hypothesis: a pragmatic proxy for homophily in online social networksSocial Network Analysis and Mining10.1007/s13278-014-0216-24:1Online publication date: 22-Aug-2014
      • (2014)Academia.eduJournal of the Association for Information Science and Technology10.1002/asi.2303865:4(721-731)Online publication date: 1-Apr-2014
      • (2013)Semantics of User Interaction in Social MediaComplex Networks IV10.1007/978-3-642-36844-8_2(13-25)Online publication date: 2013
      • (2012)Understanding and leveraging tag-based relations in on-line social networksProceedings of the 23rd ACM conference on Hypertext and social media10.1145/2309996.2310035(229-238)Online publication date: 25-Jun-2012
      • (2012)Face-to-Face Contacts at a Conference: Dynamics of Communities and RolesModeling and Mining Ubiquitous Social Media10.1007/978-3-642-33684-3_2(21-39)Online publication date: 2012
      • (2011)Face-to-face contacts at a conferenceProceedings of the 2011th International Conference on Modeling and Mining Ubiquitous Social Media - 2011 International Workshop on Modeling Social Media and 2011 International Workshop on Mining Ubiquitous and Social Environments10.5555/3120657.3120659(21-39)Online publication date: 9-Oct-2011
      • (2010)Community assessment using evidence networksProceedings of the 2010 international conference on Analysis of social media and ubiquitous data10.5555/2035637.2035642(79-98)Online publication date: 13-Jun-2010
      • (2010)Community assessment using evidence networksProceedings of the 2010th International Conference on Analysis of Social Media and Ubiquitous Data10.1007/978-3-642-23599-3_5(79-98)Online publication date: 13-Jun-2010

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