TY - GEN
T1 - Computational analysis of connectivity games with applications to the investigation of terrorist networks
AU - Michalak, Tomasz P.
AU - Rahwan, Talal
AU - Jennings, Nicholas R.
AU - Szczepański, Piotr L.
AU - Skibski, Oskar
AU - Narayanam, Ramasuri
AU - Wooldridge, Michael J.
PY - 2013
Y1 - 2013
N2 - We study a recently developed centrality metric to identify key players in terrorist organisations due to Lindelauf et al. [2013]. This metric, which involves computation of the Shapley value for connectivity games on graphs proposed by Amer and Gimenez [2004], was shown to produce substantially better results than previously used standard centralities. In this paper, we present the first computational analysis of this class of coalitional games, and propose two algorithms for computing Lindelauf et al.'s centrality metric. Our first algorithm is exact, and runs in time linear by number of connected subgraphs in the network. As shown in the numerical simulations, our algorithm identifies key players in the WTC 9/11 terrorist network, constructed of 36 members and 125 links, in less than 40 minutes. In contrast, a general-purpose Shapley value algorithm would require weeks to solve this problem. Our second algorithm is approximate and can be used to study much larger networks.
AB - We study a recently developed centrality metric to identify key players in terrorist organisations due to Lindelauf et al. [2013]. This metric, which involves computation of the Shapley value for connectivity games on graphs proposed by Amer and Gimenez [2004], was shown to produce substantially better results than previously used standard centralities. In this paper, we present the first computational analysis of this class of coalitional games, and propose two algorithms for computing Lindelauf et al.'s centrality metric. Our first algorithm is exact, and runs in time linear by number of connected subgraphs in the network. As shown in the numerical simulations, our algorithm identifies key players in the WTC 9/11 terrorist network, constructed of 36 members and 125 links, in less than 40 minutes. In contrast, a general-purpose Shapley value algorithm would require weeks to solve this problem. Our second algorithm is approximate and can be used to study much larger networks.
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M3 - Conference contribution
AN - SCOPUS:84896062539
SN - 9781577356332
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 293
EP - 301
BT - IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
T2 - 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
Y2 - 3 August 2013 through 9 August 2013
ER -