A joint classification method to integrate scientific and social networks

Mahmood Neshati, Ehsaneddin Asgari, Djoerd Hiemstra, Hamid Beigy

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
32 Downloads (Pure)


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.
Original languageUndefined
Title of host publication35th European Conference on IR Research, ECIR 2013
Place of PublicationBerlin
Number of pages12
ISBN (Print)978-3-642-36972-8
Publication statusPublished - Mar 2013
Event35th European Conference on Information Retrieval, ECIR 2013: (IR Resarch) - Moscow, Russian Federation
Duration: 24 Mar 201327 Mar 2013
Conference number: 35

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference35th European Conference on Information Retrieval, ECIR 2013
Abbreviated titleECIR
Country/TerritoryRussian Federation


  • EWI-24059
  • METIS-300201
  • IR-88459

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