论文标题
链接建议：它们对网络结构和少数民族的影响
Link recommendations: Their impact on network structure and minorities
论文作者
论文摘要
基于网络的人员建议算法被广泛在网络上使用，以建议社交媒体或专业平台中的新连接。尽管此类建议将人们聚集在一起，但算法和网络结构变化之间的反馈循环可能会加剧社会偏见。这些偏见包括丰富的富含效应，滤泡和极化。但是，社交网络是各种复杂的系统，建议可能会对它们的结构特性有所不同。在这项工作中，我们通过系统地将它们随着时间的推移应用于不同的合成网络来探讨五个建议算法。特别是，我们衡量这些建议在多大程度上改变了两人填充网络的结构，并显示了这些变化如何影响少数群体。我们的系统实验有助于更好地了解何时链接推荐算法对社交网络中的少数群体有益或有害。特别是，我们的发现表明，尽管所有算法都倾向于关闭三角形并增加内聚力，但除节点2VEC以外的所有算法都容易受到青睐，并建议具有高度的节点。此外，我们发现，尤其是在两个类都是异性词的情况下，建议算法可以降低少数族裔的可见性。
Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group. Our systematic experimentation helps to better understand when link recommendation algorithms are beneficial or harmful to minority groups in social networks. In particular, our findings suggest that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree. Furthermore, we found that, especially when both classes are heterophilic, recommendation algorithms can reduce the visibility of minorities.
