ABSTRACT Link prediction is one of the most important personalized services in social network platforms. The key point is to predict the probability of the existence of a link between two nodes based on various information in the network. This article combines information of the network structure with the user-generated contents. We propose link prediction indices based on both network structure and topic distribution (NSTD). In contrast to previous literatures, this approach makes full use of the network characteristics, such as homophily, transitivity, clustering, and degree heterogeneity. And we combine these characteristics with topic similarity when constructing indices based on both directly and indirectly connected nodes. Experiment results demonstrate that the proposed method outperforms the previous methods.

中文翻译：

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大规模有向网络中结合网络结构和主题分布的链接预测

摘要 链接预测是社交网络平台中最重要的个性化服务之一。关键是根据网络中的各种信息预测两个节点之间存在链路的概率。本文将网络结构信息与用户生成的内容相结合。我们提出了基于网络结构和主题分布（NSTD）的链接预测指标。与以往的文献相比，这种方法充分利用了网络的特性，如同质性、传递性、聚类和程度异质性。在基于直接和间接连接的节点构建索引时，我们将这些特征与主题相似性相结合。实验结果表明，所提出的方法优于以前的方法。