Abstract
One of the fundamental research challenges in network science is the centrality analysis, i.e., identifying the nodes that play the most important roles in the network. In this paper, we focus on the game-theoretic approach to centrality analysis. While various centrality indices have been proposed based on this approach, it is still unknown what distinguishes this family of indices from the more classical ones. In this paper, we answer this question by providing the first axiomatic characterization of game-theoretic centralities. Specifically, we show that every centrality can be obtained following the game-theoretic approach, and show that two natural classes of game-theoretic centrality can be characterized by two intuitive properties pertaining to Myerson's notion of Fairness.
Original language | English (US) |
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Pages | 698-705 |
Number of pages | 8 |
State | Published - 2017 |
Event | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: Feb 4 2017 → Feb 10 2017 |
Other
Other | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
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Country/Territory | United States |
City | San Francisco |
Period | 2/4/17 → 2/10/17 |
ASJC Scopus subject areas
- Artificial Intelligence