How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
This work addresses the problem of understanding the impact of link prediction models on network structure for researchers and practitioners working with dynamic networks.
This study tackles the problem of understanding how predicted links influence network evolution, and finds that a proposed temporal framework can disentangle intrinsic interaction tendencies from algorithmic feedback effects. The framework's instantaneous bias measure reliably reflects these effects across different link prediction strategies.
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.