NDLI: Potential collaboration discovery using document clustering and community structure detection

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Potential collaboration discovery using document clustering and community structure detection

Content Provider	ACM Digital Library
Author	de Lima, Beatriz S.L.P. Evsukoff, Alexandre Gonçalves Ebecken, Nelson Francisco Favilla dos Santos, Cristian Klen
Abstract	Complex network analysis is a growing research area in a wide variety of domains and has recently become closely associated with data, text and web mining. One of the most active areas in the study of complex networks is the detection of community structure, which can be related to the clustering problem in data mining. This paper employs a community structure detection algorithm for document clustering in order to discover potential relationships in a social network. The proposed approach is explored in a case study of potential collaboration discovery among the research staff in the Graduate Civil Engineering Department of the Federal University of Rio de Janeiro, Brazil. The results show that the combined use of both techniques provides useful insights on the relationships, both existent and potential, among individuals in the social network.
Starting Page	39
Ending Page	46
Page Count	8
File Format	PDF
ISBN	9781605588070
DOI	10.1145/1651274.1651283
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Text mining Complex networks Documents clustering Community structure detection Spectral clustering
Content Type	Text
Resource Type	Article

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