Who Will Follow Whom? Exploiting Semantics for Link Prediction in Attention-Information Networks

  • Matthew Rowe
  • Milan Stankovic
  • Harith Alani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)


Existing approaches for link prediction, in the domain of network science, exploit a network’s topology to predict future connections by assessing existing edges and connections, and inducing links given the presence of mutual nodes. Despite the rise in popularity of Attention-Information Networks (i.e. microblogging platforms) and the production of content within such platforms, no existing work has attempted to exploit the semantics of published content when predicting network links. In this paper we present an approach that fills this gap by a) predicting follower edges within a directed social network by exploiting concept graphs and thereby significantly outperforming a random baseline and models that rely solely on network topology information, and b) assessing the different behaviour that users exhibit when making followee-addition decisions. This latter contribution exposes latent factors within social networks and the existence of a clear need for topical affinity between users for a follow link to be created.


Cosine Similarity Link Prediction Concept Graph Conc Graph Follower Decision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matthew Rowe
    • 1
    • 2
  • Milan Stankovic
    • 3
    • 4
  • Harith Alani
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityMilton KeynesUK
  2. 2.School of Computing and CommunicationsLancaster UniversityLancasterUK
  3. 3.Hypios ResearchParisFrance
  4. 4.Universit Paris-SorbonneParisFrance

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