How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs

2026-03-04

AI Summary

This work addresses the challenge that existing link prediction models struggle to disentangle users’ intrinsic preferences from the amplification effect of algorithmic feedback on network homophily. The authors propose the first dynamic graph analysis framework based on a multivariate Hawkes process, which explicitly decouples users’ inherent interaction tendencies from algorithmic feedback mechanisms. Central to this approach is a novel bias metric driven by instantaneous interaction intensity, capturing real-time reinforcement dynamics beyond conventional cumulative measures. The framework is theoretically grounded, with formal proofs establishing the stability and convergence of the induced dynamics. Experimental results demonstrate that the proposed bias metric effectively quantifies the strength of algorithmic feedback under diverse link prediction strategies, offering a reliable tool for understanding how algorithms shape network evolution.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Preferences

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Link prediction models are increasingly used to recommend interactions in evolving networks, yet their impact on network structure is typically assessed from static snapshots. In particular, observed homophily conflates intrinsic interaction tendencies with amplification effects induced by network dynamics and algorithmic feedback. We propose a temporal framework based on multivariate Hawkes processes that disentangles these two sources and introduce an instantaneous bias measure derived from interaction intensities, capturing current reinforcement dynamics beyond cumulative metrics. We provide a theoretical characterization of the stability and convergence of the induced dynamics, and experiments show that the proposed measure reliably reflects algorithmic feedback effects across different link prediction strategies.

Problem

• link prediction
• algorithmic feedback
• network evolution
• homophily
• dynamic graphs

Innovation

• link prediction
• algorithmic feedback
• temporal networks
• Hawkes processes
• homophily