Review of Various Algorithms in Graph Mining Based on Search Strategies
Vijender Singh1, Deepak Garg2
1Vijender Singh, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India.
2Deepak Garg, Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology University, Patiala, Punjab, India.
Manuscript received on January 01, 2016. | Revised Manuscript received on January 11, 2016. | Manuscript published on January 15, 2016. | PP: 20-27 | Volume-4 Issue-2, January 2016. | Retrieval Number: B0959014216/2016©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: Graph mining is an active research area during these days. Graphs have become significant in the modeling of complicated structures such as circuit images, chemical compounds, protein structures, biological networks, social networks, web workflows and XML documents. A common framework is necessary to study various graph mining algorithms and their applications. In this paper, we present a review study of various algorithms based on their graph representation, subgraph generation, algorithm approach, frequency evaluation and search strategy.
Keywords: Subgraphs, Graph Mining, Algorithms.