Discovering Links among Social Networks

  • Francesco Buccafurri
  • Gianluca Lax
  • Antonino Nocera
  • Domenico Ursino
Conference paper

DOI: 10.1007/978-3-642-33486-3_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7524)
Cite this paper as:
Buccafurri F., Lax G., Nocera A., Ursino D. (2012) Discovering Links among Social Networks. In: Flach P.A., De Bie T., Cristianini N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science, vol 7524. Springer, Berlin, Heidelberg

Abstract

Distinct social networks are interconnected via bridge users, who play thus a key role when crossing information is investigated in the context of Social Internetworking analysis. Unfortunately, not always users make their role of bridge explicit by specifying the so-called me edge (i.e., the edge connecting the accounts of the same user in two distinct social networks), missing thus a potentially very useful information. As a consequence, discovering missing me edges is an important problem to face in this context yet not so far investigated. In this paper, we propose a common-neighbors approach to detecting missing me edges, which returns good results in real life settings. Indeed, an experimental campaign shows both that the state-of-the-art common-neighbors approaches cannot be effectively applied to our problem and, conversely, that our approach returns precise and complete results.

Keywords

Link Prediction Link Mining Social networks Social Internetworking 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francesco Buccafurri
    • 1
  • Gianluca Lax
    • 1
  • Antonino Nocera
    • 1
  • Domenico Ursino
    • 1
  1. 1.DIMETUniversity “Mediterranea” of Reggio CalabriaReggio CalabriaItaly

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