Design and Development of Abstractness in Graph Mining Technique using Structural Datum
S. P. Victor1, M. Antony Sundar Singh2
1Dr.S.P.Victor, HOD / Department of Computer Science St.Xaviers College, Tiruelveli Tamilandu,India.
2M.Antony Sundar Singh, Research Scholar Manonmaniam Sundaranar University, Tirunelveli. Tamilnadu, India.
Manuscript received on June 06, 2013. | Revised Manuscript received on June 29, 2013. | Manuscript published on July 05, 2013. | PP: 74-76 | Volume-3 Issue-3, July 2013. | Retrieval Number: B1442053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Graphs are everywhere, ranging from social networks and mobile call networks to biological net-works and the World Wide Web. Mining big graphs leads too many interesting applications including cyber security, fraud detection, Web search, recommendation, and many more. In this paper we describe a technique for the conversion of real-time environment to a Graph Mining pattern. We analyze very large, real world graphs with billions of nodes and edges. Our findings include digraph structures in the connected component size distribution. In the future we will extend our research to propose a Graph Template Converter for any real-time complex entities.
Keywords: Graph mining, Graph pattern, Graph template, Graph network.