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Navigation in Online Social Networks

Navigation in Online Social Networks

Mehran Asadi, Afrand Agah
Copyright: © 2015 |Pages: 14
ISBN13: 9781466685055|ISBN10: 1466685050|EISBN13: 9781466685062
DOI: 10.4018/978-1-4666-8505-5.ch016
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MLA

Asadi, Mehran, and Afrand Agah. "Navigation in Online Social Networks." Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, edited by Noor Zaman, et al., IGI Global, 2015, pp. 328-341. https://doi.org/10.4018/978-1-4666-8505-5.ch016

APA

Asadi, M. & Agah, A. (2015). Navigation in Online Social Networks. In N. Zaman, M. Seliaman, M. Hassan, & F. Marquez (Eds.), Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence (pp. 328-341). IGI Global. https://doi.org/10.4018/978-1-4666-8505-5.ch016

Chicago

Asadi, Mehran, and Afrand Agah. "Navigation in Online Social Networks." In Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, edited by Noor Zaman, et al., 328-341. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8505-5.ch016

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Abstract

In this chapter, we investigate the role of influential people in an Online Social Network. We introduce a navigation approach to locate influential nodes in Online Social Networks. The purpose of quantifying influence in Online Social Networks is to determine influence within the members of an Online Social Networks and then using influence for navigation in such networks. We find out that we can take advantage of influential people in Online Social Networks to reach a target node in such networks. We utilize total number of direct friends of each node, total number of shared neighbors, the total number of common attributes, the total number of unique attributes, the distance to target node, and past visited nodes. We present an algorithm that takes advantage of influential people to reach a target in the network. Our navigation algorithm returns a path between two nodes in an average of ten percent less iterations, with a maximum of eighty percent less iterations, and only relies on public attributes of a node in the network.

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