A joint classification method to integrate scientific and social networks

Authors:
Mahmood Neshati;Ehsaneddin Asgari;Djoerd Hiemstra;Hamid Beigy
Affiliations:
Department of Computer Engineering, Sharif University of Technology, Iran;School of Computer and Communication Science (IC), Ecole Polytechnique Fédéral de Lausanne - EPFL, Switzerland;Database Research Group, University of Twente, The Netherlands;Department of Computer Engineering, Sharif University of Technology, Iran
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

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Abstract

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.