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A Method of Subgraphs Extraction in a Large Graph Database in a Distributed System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 5 |
| Year of Publication: 2017 |
| Authors: Ritu Yadav, Samarth Varshney |
10.5120/ijca2017914960
|
Ritu Yadav, Samarth Varshney . A Method of Subgraphs Extraction in a Large Graph Database in a Distributed System. International Journal of Computer Applications. 175, 5 ( Oct 2017), 1-5. DOI=10.5120/ijca2017914960
Abstract
Since many real applications such as web connectivity, social networks, and so on, are emerging now-a-days, thus graph databases have been commonly used as significant tools to exemplify and query complex graph data wherein each vertex in a graph usually contains information, which can be modeled by a set of tokens or elements. The method for subgraphs extraction by considering set similarity query over a large graph database has already been proposed, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic/fixed) weighted set similarity in a centralized system. This paper explains the efficient implementation of subgraphs extraction in a large graph database in a distributed environment by considering both vertex set similarity and graph topology which offers a better price/performance ratio and increases availability using redundancy when parts of a system fail than centralized systems in case of a large dataset (i.e., a graph with millions/billions of nodes wherein each node contains some information) by performing parallel processing.
References
- Z. Sun, H. Wang, H. Wang, B. Shao, and J. Li, “Efficient subgraph matching on billion node graphs,” Proc. VLDB Endowment, vol. 5, no. 9, pp. 788–799, 2012.
- P. Zhao and J. Han, “On graph query optimization in large networks,” Proc. VLDB Endowment, vol. 3, nos. 1/2, pp. 340–351, 2010.
- L. Zou, L. Chen, and M. T. Ozsu, “Distance-join: Pattern match query in a large graph database,” Proc. VLDB Endowment, vol. 2, no. 1, pp. 886–897, 2009.
- Y. Tian and J. M. Patel, “Tale: A tool for approximate large graph matching,” in Proc. 24th Int. Conf. Data Eng., 2008, pp. 963–972.
- H. He and A. K. Singh, “Closure-tree: An index structure for graph queries,” in Proc. 222nd Int. Conf. Data Eng., 2006, p. 38.
- W.-S. Han, J. Lee, and J.-H. Lee, “Turboiso: Towards ultrafast and robust subgraph isomorphism search in large graph databases,” in Proc. ACM SIGMOD Int. Conf. Manage. Data, 2013, pp. 337–348.
- L. P. Cordella, P. Foggia, C. Sansone, and M. Vento, “A (sub) graph isomorphism algorithm for matching large graphs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 10, pp. 1367–1372, Oct. 2004.
- J. R. Ullmann, “An algorithm for subgraph isomorphism,” J. ACM, vol. 23, no. 1, pp. 31–42, 1976.
- S. Ma, Y. Cao, W. Fan, J. Huai, and T. Wo, “Capturing topology in graph pattern matching,” Proc. VLDB Endowments, vol. 5, no. 4, pp. 310–321, 2012.
- A. Khan, Y. Wu, C. C. Aggarwal, and X. Yan, “Nema: Fast graph search with label similarity,” in Proc. VLDB Endowment, vol. 6, no. 3, pp. 181–192, 2013.
- Liang Hong, Member, IEEE, Lei Zou, Member, IEEE, Xiang Lian, Member, IEEE, and Philip S. Yu, Fellow, IEEE, "Subgraph Matching with Set Similarity in a Large Graph Database", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 27, NO. 9, SEPTEMBER 2015
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