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VGraph: graph virtualization towards big data

Khan, Maqbool, Liu, Meng, Dou, Wanchun and Yu, Shui 2015, VGraph: graph virtualization towards big data, in CBD 2015: Proceedings of the 2015 3rd International Conference on Advanced Cloud and Big Data, IEEE, Piscataway, N.J., pp. 153-158, doi: 10.1109/CBD.2015.33.

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Title VGraph: graph virtualization towards big data
Author(s) Khan, Maqbool
Liu, Meng
Dou, Wanchun
Yu, ShuiORCID iD for Yu, Shui orcid.org/0000-0003-4485-6743
Conference name Advanced Cloud and Big Data. Conference (3rd : 2015 : Yangzhou, Jiangsu, China)
Conference location Yangzhou, Jiangsu, China
Conference dates 2015/10/30 - 2015/11/01
Title of proceedings CBD 2015: Proceedings of the 2015 3rd International Conference on Advanced Cloud and Big Data
Publication date 2015
Start page 153
End page 158
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) virtual graph
vGraph
virtual nodes
virtual edges
massive graphs
Big Data graphs
Summary Virtualization brought an immense commute in the modern technology especially in computer networks since last decade. The enormity of big data has led the massive graphs to be increased in size exponentially in recent years so that normal tools and algorithms are going weak to process it. Size diminution of the massive graphs is a big challenge in the current era and extraction of useful information from huge graphs is also problematic. In this paper, we presented a concept to design the virtual graph vGraph in the virtual plane above the original plane having original massive graph and proposed a novel cumulative similarity measure for vGraph. The use of vGraph is utile in lieu of massive graph in terms of space and time. Our proposed algorithm has two main parts. In the first part, virtual nodes are designed from the original nodes based on the calculation of cumulative similarity among them. In the second part, virtual edges are designed to link the virtual nodes based on the calculation of similarity measure among the original edges of the original massive graph. The algorithm is tested on synthetic and real-world datasets which shows the efficiency of our proposed algorithms.
ISBN 9781467385374
Language eng
DOI 10.1109/CBD.2015.33
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085410

Document type: Conference Paper
Collection: School of Information Technology
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