Link prediction in complex networks predicts the possibility of link generation between two nodes that have not been linked yet in the network, based on known network structure and attributes. It can be applied in various fields, such as friend recommendation in social networks and prediction of protein-protein interaction in biology. However, in the social network, link prediction may raise concerns about privacy and security, because, through link prediction algorithms, criminals can predict the friends of an account user and may even further discover private information such as the address and bank accounts. Therefore, it is urgent to develop a strategy to prevent being identified by link prediction algorithms and protect privacy, utilizing perturbation on network structure at a low cost, including changing and adding edges. This article mainly focuses on the influence of network structural preference perturbation through deletion on link prediction. According to a large number of experiments on the various real networks, edges between large-small degree nodes and medium-medium degree nodes have the most significant impact on the quality of link prediction.

中文翻译：

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网络结构偏好对链路预测的影响

复杂网络中的链接预测会根据已知的网络结构和属性，预测在网络中尚未链接的两个节点之间生成链接的可能性。它可以应用于各种领域，例如社交网络中的朋友推荐以及生物学中蛋白质相互作用的预测。但是，在社交网络中，链接预测可能会引起人们对隐私和安全性的担忧，因为犯罪分子可以通过链接预测算法预测帐户用户的朋友，甚至可能进一步发现私人信息，例如地址和银行帐户。因此，迫切需要开发一种策略来防止被链接预测算法识别并保护隐私，并以低成本利用对网络结构的干扰，包括改变和增加边缘。本文主要关注通过删除对网络结构偏好的扰动对链路预测的影响。根据各种实际网络上的大量实验，大小节点与中度节点之间的边缘对链路预测的质量影响最大。