Network-based ranking in social systems: three challenges

TL;DR

This paper discusses three fundamental challenges in network-based ranking algorithms within social systems, focusing on biases, limited effectiveness, and systemic risks, and suggests network science and agent-based modeling as solutions.

Contribution

It identifies key challenges in applying network-based ranking algorithms to real-world social systems and proposes methods to address these issues.

Findings

Ranking biases can distort importance assessments.

Effectiveness of rankings varies across different problems.

Ranking-driven decisions may cause harmful feedback loops.

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: (i) 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.