TY - JOUR
T1 - Axiomatic characterization of game-theoretic centrality
AU - Skibski, Oskar
AU - Michalak, Tomasz P.
AU - Rahwan, Talal
N1 - Funding Information:
This article is an extended version of a paper originally presented at AAAI-17 (Skibski, Michalak, & Rahwan, 2017) with a number of new technical results. Specifically, all the results regarding the class of edge-induced game-theoretic centralities are new (Section 7). Furthermore, we added proofs of Lemma 2, 4, 5, 7, 9, 11 and full proofs (instead of sketches) of Theorem 6, 8, 10, 12 (Sections 4–6). Finally, we extended the related work section (Section 2) and added the discussion of the implications of our work (Section 8). Oskar Skibski was supported by the Foundation for Polish Science within the Homing programme financed from the Smart Growth Operational Programme (Project title: “Centrality Measures: from Theory to Applications”). Tomasz Michalak and Talal Rahwan were supported by the Polish National Science Center grant DEC-2013/09/D/ST6/03920. Tomasz Michalak was also supported by the European Research Council under Advanced Grant 291528 (“RACE”) for the earlier version of this work.
Funding Information:
Oskar Skibski was supported by the Foundation for Polish Science within the Homing programme financed from the Smart Growth Operational Programme (Project title: “Centrality Measures: from Theory to Applications”). Tomasz Michalak and Talal Rahwan were supported by the Polish National Science Center grant DEC-2013/09/D/ST6/03920. Tomasz Michalak was also supported by the European Research Council under Advanced Grant 291528 (“RACE”) for the earlier version of this work.
Publisher Copyright:
© 2018 AI Access Foundation. All rights reserved.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - 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.
AB - 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.
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U2 - 10.1613/jair.1.11202
DO - 10.1613/jair.1.11202
M3 - Article
AN - SCOPUS:85048048438
SN - 1076-9757
VL - 62
SP - 33
EP - 68
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -