Deakin University

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VGraph: graph virtualization towards big data

conference contribution
posted on 2015-01-01, 00:00 authored by M Khan, M Liu, W Dou, Shui Yu
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.



Advanced Cloud and Big Data. Conference (3rd : 2015 : Yangzhou, Jiangsu, China)


153 - 158




Yangzhou, Jiangsu, China

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

CBD 2015: Proceedings of the 2015 3rd International Conference on Advanced Cloud and Big Data