区分算法推荐与用户本性对网络演化的影响，揭示推荐机制如何改变社交关系形成。
How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs
- 用多变量霍克斯过程建模动态网络，分离用户偏好与算法反馈
- 提出瞬时偏差度量，实时捕捉推荐带来的强化效应
- 验证不同推荐策略下算法影响的稳定性与收敛性，适合网络分析与推荐系统研究者
链接预测模型在动态网络中越来越多地用于推荐交互，但其对网络结构的影响通常仅通过静态快照评估。特别是观察到的同质性混淆了内在互动倾向与网络动态及算法反馈所引发的放大效应。我们提出一种基于多变量霍克斯过程的时间框架，以分离这两种来源，并引入一个源自交互强度的瞬时偏差度量，捕捉当前的强化动态，超越累积指标。我们提供了所诱导动态的稳定性和收敛性的理论表征，实验表明该度量在不同链接预测策略下能可靠反映算法反馈效应。
原文摘要 · Abstract (English)
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.
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