An Axiom System for Feedback Centralities
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 443-449.
https://doi.org/10.24963/ijcai.2021/62
Keywords
- Machine Learning Applications: Networks
- Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems
Abstract
In recent years, the axiomatic approach to centrality measures has attracted attention in the literature. However, most papers propose a collection of axioms dedicated to one or two considered centrality measures. In result, it is hard to capture the differences and similarities between various measures. In this paper, we propose an axiom system for four classic feedback centralities: Eigenvector centrality, Katz centrality, Katz prestige and PageRank. We prove that each of these four centrality measures can be uniquely characterized with a subset of our axioms. Our system is the first one in the literature that considers all four feedback centralities.
@inproceedings{ijcai2021p62,
title = {An Axiom System for Feedback Centralities},
author = {Wąs, Tomasz and Skibski, Oskar},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {443--449},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/62},
url = {https://doi.org/10.24963/ijcai.2021/62},
}
TY - CPAPER TI - An Axiom System for Feedback Centralities AU - Wąs, Tomasz AU - Skibski, Oskar T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 443 EP - 449 DO - 10.24963/ijcai.2021/62 UR - https://doi.org/10.24963/ijcai.2021/62 ER -Copied
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