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NodeRank: Finding influential nodes in social networks based on interests

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Abstract

Finding influential members in social networks received a lot of interest in recent literature. Several algorithms have been proposed that provide techniques for extracting a set of the most influential people in a certain social network. However, most of these algorithms find influential nodes based solely on the topological structure of the network. In this paper, a new algorithm, namely NodeRank, is proposed that ranks every user in a given social network based on the topological structure as well as the interests of the users (nodes). Higher ranks are given to people with great influence on other members of the network. Furthermore, the paper investigates a MapReduce version of the algorithm that enables the algorithm to run on multiple machines simultaneously. Experiments showed that the MapReduce model is not suitable for the NodeRank algorithm since MapReduce is only applicable for batch processes and the NodeRank is highly iterative. For that reason, a parallel version of the algorithm is proposed that utilizes Hadoop Spark, a framework for parallel processes that supports batch operations as well as iterative and recursive algorithms. Several experiments have been carried out to test the accuracy as well as the scalability of the algorithm.

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Authors and Affiliations

  1. University of Sharjah, Sharjah, UAE

    Mohammed Bahutair, Zaher Al Aghbari & Ibrahim Kamel

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  1. Mohammed Bahutair
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  2. Zaher Al Aghbari
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  3. Ibrahim Kamel
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Correspondence to Zaher Al Aghbari.

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Bahutair, M., Al Aghbari, Z. & Kamel, I. NodeRank: Finding influential nodes in social networks based on interests. J Supercomput 78, 2098–2124 (2022). https://doi.org/10.1007/s11227-021-03947-6

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