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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 345-348
Kifayat Ullah Khan , Dept. of Computer Engineering, Kyung Hee University, Republic of Korea
Mostofa Kamal Rasel , Dept. of Computer Engineering, Kyung Hee University, Republic of Korea
Muhammad Noorulamin , Dept. of Computer Engineering, Kyung Hee University, Republic of Korea
Waqas Nawaz , Dainfos Lab, Innopolis University, Russia
Young-Koo Lee , Dept. of Computer Engineering, Kyung Hee University, Republic of Korea
ABSTRACT
Graph summarization is a well known technique to create summary of mega-sized structures like social networks and world wide web. A prime bottleneck in this process is in-efficient pairwise similarity computation strategy to find similar nodes for compression. Previous work provides a scalable similarity computation strategy by using Locality Sensitive Hashing (LSH) to improve execution time of pairwise methods for summary of a static graph. Whereas LSH adoption provides desired acceleration, however, it requires large storage space for indexing candidate similar nodes. This problem becomes even more challenging in case of a dynamic graph, increasing the space complexity from O (b.n) to O (b.n.k), where b, n, and k are number of hash tables, total nodes, and snapshots of the graph respectively. In this paper, we propose a new index structure for LSH to align candidate similar nodes from a dynamic graph, with least storage space complexity. The proposed structure reduces space requirements by a factor ?, where range of ? depends on structural redundancy of graphs. We evaluate our proposed solution for summarization of four real world dynamic graphs and obtain compression upto 52%.
INDEX TERMS
Indexing, Complexity theory, Encoding, Electronic mail, Redundancy, Social network services
CITATION

K. U. Khan, M. K. Rasel, M. Noorulamin, W. Nawaz and Y. Lee, "On efficiently summarizing a large dynamic graph," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 345-348.
doi:10.1109/BIGCOMP.2016.7425944
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