Using Social Interactions Network Graph and Centrality to Identify Key Players
Alex Pongpech
Alex Pongpech, Big Data Engineering Graduate Program, Dhurakijpundit University, Thailand.
Manuscript received on 29 January 2021 | Revised Manuscript received on 01 February 2021 | Manuscript Accepted on 15 February 2021 | Manuscript published on 28 February 2021 | PP: 10-15 | Volume-1 Issue-1, February 2021 | Retrieval Number:A1002011121/2021©LSP
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Topic on Trend has been made more popular recently with the published Food trend 2016 by Google. Prior to the social network era, difficulty in predicting and identifying trend are difficult at best. This is mainly due to difficulty of gathering data from the public to do the analysis. Given that graph can be utilized to modeled social network users and their relationships, and that graph algorithms are very mature. The possibility of utilizing graph algorithms to analyze social network users to help identifying trendsetters is worth investigating. In this paper, the aim is to apply graph theory to model interactions on social network. The model can then be utilized to identify key players based on the Betweenness centrality and Page Rank centrality. Finally, based on Page Rank algorithm, vertexes ranking is implemented using python.
Keywords: Trend, Social Network, Graph, Centrality, Key player, Page Rank.