2020/05/29 by Manuel Sebastian Mariani, Manuel S. Mariani, Linyuan Lü · 14 citations
Computer Science · Decision Sciences · Physics and Astronomy · #Artificial intelligence #Complex Network Analysis Techniques #Complex network #Computer science #Data science #Face (sociological concept) #Game Theory and Applications #Identification (biology) #Machine learning #Network science #Opinion Dynamics and Social Influence #Perspective (graphical) #Ranking (information retrieval) #Recommender system #Social science #Sociology #World Wide Web #cs.CY #cs.SI #physics.soc-ph
paper · pdf · doi:10.1088/2632-072x/ab8a61
published in Journal of Physics Complexity 1(1), 011001 (IOP Publishing) · Perspective article. 9 pages, 3 figures
arxiv created 2020/05/29 · openalex publication_date 2020/05/29 · arxiv updated 2020/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.