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Efficient Techniques for Graph Searching and Biological Network Mining

Efficient Techniques for Graph Searching and Biological Network Mining

Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti, Dennis Shasha
Copyright: © 2012 |Pages: 23
ISBN13: 9781613500538|ISBN10: 161350053X|EISBN13: 9781613500545
DOI: 10.4018/978-1-61350-053-8.ch005
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MLA

Ferro, Alfredo, et al. "Efficient Techniques for Graph Searching and Biological Network Mining." Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, IGI Global, 2012, pp. 89-111. https://doi.org/10.4018/978-1-61350-053-8.ch005

APA

Ferro, A., Giugno, R., Pulvirenti, A., & Shasha, D. (2012). Efficient Techniques for Graph Searching and Biological Network Mining. In S. Sakr & E. Pardede (Eds.), Graph Data Management: Techniques and Applications (pp. 89-111). IGI Global. https://doi.org/10.4018/978-1-61350-053-8.ch005

Chicago

Ferro, Alfredo, et al. "Efficient Techniques for Graph Searching and Biological Network Mining." In Graph Data Management: Techniques and Applications, edited by Sherif Sakr and Eric Pardede, 89-111. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-053-8.ch005

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Abstract

From biochemical applications to social networks, graphs represent data. Comparing graphs or searching for motifs on such data often reveals interesting and useful patterns. Most of the problems on graphs are known to be NP-complete. Because of the computational complexity of subgraph matching, reducing the candidate graphs or restricting the space in which to search for motifs is critical to achieving efficiency. Therefore, to optimize and engineer isomorphism algorithms, design indexing and suitable search methods for large graphs are the main directions investigated in the graph searching area. This chapter focuses on the key concepts underlying the existing algorithms. First it reviews the most known used algorithms to compare two algorithms and then it describes the algorithms to search on large graphs making emphasis on their application on biological area.

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