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Axiomatic Characterization of Game-Theoretic Centrality
Oskar Skibski
Tomasz P Michalak
Talal Rahwan
Journal of Artificial Intelligence Research, 2018, 62: 33-68.
<jats:p>&#x0D; &#x0D; &#x0D; One of the fundamental research challenges in network science is centrality analysis, i.e., identifying the nodes that play the most important roles in the network. In this article, we focus on the game-theoretic approach to centrality analysis. While various centrality indices have been recently proposed based on this approach, it is still unknown how general is the game-theoretic approach to centrality and what distinguishes some game-theoretic centralities from others. In this article, we attempt to answer this question by providing the first axiomatic characterization of game-theoretic centralities. Specifically, we show that every possible centrality measure can be obtained following the game-theoretic approach. Furthermore, we study three natural classes of game-theoretic centrality, and prove that they can be characterized by certain intuitive properties pertaining to the well-known notion of Fairness due to Myerson.&#x0D; &#x0D; &#x0D; </jats:p>
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