A Joint Classification Method to Integrate Scientific and Social Networks

  • Mahmood Neshati
  • Ehsaneddin Asgari
  • Djoerd Hiemstra
  • Hamid Beigy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)


In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs, then the problem of name disambiguation is reduced to predict the label of each candidate pair as either true or false matching. By defining matching dependency graph, we propose a joint label prediction model to determine the label of all candidate pairs simultaneously. An extensive set of experiments have been conducted on six test collections obtained from the DBLP and the Twitter networks to show the effectiveness of the proposed joint label prediction model.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mahmood Neshati
    • 1
  • Ehsaneddin Asgari
    • 3
  • Djoerd Hiemstra
    • 2
  • Hamid Beigy
    • 1
  1. 1.Department of Computer EngineeringSharif University of TechnologyIran
  2. 2.Database Research GroupUniversity of TwenteThe Netherlands
  3. 3.School of Computer and Communication Science (IC)Ecole Polytechnique Fédéral de Lausanne - EPFLSwitzerland

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