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

TL;DR

This paper introduces a temporal framework using multivariate Hawkes processes to distinguish between intrinsic interaction tendencies and algorithmic feedback effects in evolving networks, providing a new measure to assess reinforcement dynamics.

Contribution

It proposes a novel temporal model and bias measure that disentangle intrinsic network behaviors from algorithmic influence, with theoretical analysis and empirical validation.

Findings

The bias measure reliably captures algorithmic feedback effects.

Theoretical analysis shows stability and convergence of the dynamics.

Experiments confirm the measure's effectiveness across strategies.

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.