Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

中文翻译：

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基于社区发现和用户行为偏好的在线社交网络智能链路预测管理

在线社交网络中的链接预测旨在预测尚未建立好友网络的用户，其动机是基于当前网络结构和节点属性提供好友推荐。然而，许多现有的链接预测方法没有考虑社区特征、文本信息和增长机制等重要信息。在本文中，我们根据社交网络的特点提出了一种基于关系强度的智能数据管理机制，以实现在线社交网络中的可靠预测。其次，通过将用户的网络结构属性和兴趣偏好作为影响在线社交网络链接预测过程的重要因素，我们通过设计一个朋友推荐模型，将用户的关系信息和兴趣偏好特征新颖地结合到社区检测算法中，从而进一步改进预测过程。最后，在 Twitter 数据集上进行的大量实验证明了我们提出的模型在动态社区检测和链接预测方面的有效性。