DocumentCode
2991725
Title
A Large-Scale Graph Learning Framework of Technological Gatekeepers by MapReduce
Author
Tong, Liu ; Wensheng, Guo
Author_Institution
Sch. of Econ. & Manage., Beihang Univ., Beijing, China
fYear
2012
fDate
21-25 May 2012
Firstpage
1997
Lastpage
2003
Abstract
MapReduce based graph learning algorithm is applied to the identification of technological gatekeepers in large-scale technological information flow network across large number of enterprises of several industries. Experiments show that the efficiency of Multi Adjacency Matrix algorithm is one time higher than PEGASUS. The effects of the number of cluster nodes and input file formats on the performance of the algorithm are also studied.
Keywords
data mining; graph theory; information networks; matrix algebra; MapReduce; large-scale graph learning framework; large-scale technological information flow network; multiadjacency matrix algorithm; technological gatekeeper; Algorithm design and analysis; Computational modeling; Flow graphs; Industries; Logic gates; Runtime; Vectors; Graph model; MapReduce; Technological gatekeepers;
fLanguage
English
Publisher
ieee
Conference_Titel
Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International
Conference_Location
Shanghai
Print_ISBN
978-1-4673-0974-5
Type
conf
DOI
10.1109/IPDPSW.2012.248
Filename
6270407
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