论文标题
积极算法偏见不能停止同粒细胞网络中的破碎
Positive algorithmic bias cannot stop fragmentation in homophilic networks
论文作者
论文摘要
在学术和非学术世界中，分裂，回声室及其在社交网络中的改善一直是一个日益关注的问题。本文展示了在同质性的假设下，回声室和碎片是如何在高度灵活的社交网络的系统中，即使在理想的异质性条件下也是如此。我们通过找到针对Schelling模型的分析，基于网络的解决方案来实现这一目标，并证明弱关系不会阻碍这一过程。此外，我们得出的是，以重新布线形式没有阳性算法偏置水平能够防止碎裂及其对降低碎片速度的影响可忽略不计。
Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and non-academic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process. Furthermore, we derive that no level of positive algorithmic bias in the form of rewiring is capable of preventing fragmentation and its effect on reducing the fragmentation speed is negligible.
