Network-based ranking in social systems: three challenges

Author:

Mariani Manuel SORCID,Lü LinyuanORCID

Abstract

Abstract Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (1) rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents’ decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.

Funder

Natural Science Foundation of Zhejiang Province of China

Science Strength Promotion Program of the University of Electronic Science and Technology of China

Swiss National Science Foundation

National Natural Science Foundation of China

Publisher

IOP Publishing

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