Abstract
Identification of a set of key players in a given social network is of interest in many disciplines such as sociology, politics, finance, economics, etc. Although many algorithms have been proposed to identify a set of key players, each emphasizes a single objective of their interest. Consequently, the prevailing deficiency of each of these methods is that they perform well only when we consider their objective of interest as the only characteristic the set of key players should have. But in complicated real life applications, we need a set of key players which can perform well with respect to multiple objectives of interest. In this paper, we propose a new perspective for key player identification, based on optimizing multiple objectives of interest. This method allows us to compare other methods of key player identification. The sets of key players identified by this method are better when multiple objectives must be addressed. In addition we propose an algorithm to select the most suitable sets of key players when multiple choices are available. To reduce the computational complexity of the proposed approach for large networks, we propose a new sampling approach based on Degree centrality. We apply these algorithms in eventual influence limitation (EIL) problem and immunization problem and show that our multi-objective methodology outperforms previous key player identification approaches.
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Notes
All the experiments elaborated in this study were run on an Intel Core i5 @ 1.70 GHz, 8 GB RAM. The Python software package, NetworkX (http://networkx.github.io) was used to implement the algorithms.
A solution \(X\) is non-dominated, if every solution better than \(X\) with respect to one objective function, must be worse than \(X\) with respect to another objective function.
Key player identification methods of degree centrality, Betweenness centrality (Barthelemy 2004), Eigenvector centrality (Bonacich 1972), PageRank (Page et al. 1999), Borgatti’s KPP Positive (Borgatti 2006), Borgatti’s KPP Negative (Borgatti 2006), Principal Component centrality (Ilyas et al. 2011), KPP Positive using Information theory (Ortiz-Arroyo and Hussain 2008), KPP Negative using Information theory (Ortiz-Arroyo and Hussain 2008), K-shell (Kitsak et al. 2010) are compared here.
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Gunasekara, R.C., Mehrotra, K. & Mohan, C.K. Multi-objective optimization to identify key players in large social networks. Soc. Netw. Anal. Min. 5, 21 (2015). https://doi.org/10.1007/s13278-015-0260-6
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DOI: https://doi.org/10.1007/s13278-015-0260-6