Recently, link prediction has attracted more attentions from various disciplines such as computer science, bioinformatics and economics. In this problem, unknown links between nodes are discovered based on numerous information such as network topology, profile information and user generated contents. Most of the previous researchers have focused on the structural features of the networks. While the recent researches indicate that contextual information can change the network topology. Although, there are number of valuable researches which combine structural and content information, but they face with the scalability issue due to feature engineering. Because, majority of the extracted features are obtained by a supervised or semi supervised algorithm. Moreover, the existing features are not general enough to indicate good performance on different networks with heterogeneous structures. Besides, most of the previous researches are presented for undirected and unweighted networks. In this paper, a novel link prediction framework called "DeepLink" is presented based on deep learning techniques. In contrast to the previous researches which fail to automatically extract best features for the link prediction, deep learning reduces the manual feature engineering. In this framework, both the structural and content information of the nodes are employed. The framework can use different structural feature vectors, which are prepared by various link prediction methods. It considers all proximity orders that are presented in a network during the structural feature learning. We have evaluated the performance of DeepLink on two real social network datasets including Telegram and irBlogs. On both datasets, the proposed framework outperforms several structural and hybrid approaches for link prediction problem.

中文翻译：

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DeepLink：一种基于深度学习的新型链接预测框架

近年来，链接预测越来越受到计算机科学、生物信息学和经济学等各个学科的关注。在该问题中，基于网络拓扑、配置文件信息和用户生成内容等众多信息发现节点之间的未知链路。以前的大多数研究人员都专注于网络的结构特征。虽然最近的研究表明上下文信息可以改变网络拓扑。尽管有许多结合结构和内容信息的有价值的研究，但由于特征工程，它们面临着可扩展性问题。因为，大部分提取的特征是通过监督或半监督算法获得的。而且，现有的特征不足以表明在具有异构结构的不同网络上的良好性能。此外，大多数先前的研究都是针对无向和未加权网络提出的。在本文中，提出了一种基于深度学习技术的新型链接预测框架“DeepLink”。与之前的研究无法自动提取链接预测的最佳特征相比，深度学习减少了人工特征工程。在这个框架中，节点的结构和内容信息都被采用。该框架可以使用不同的结构特征向量，这些向量是通过各种链接预测方法准备的。它考虑了结构特征学习期间网络中呈现的所有邻近顺序。我们已经在两个真实的社交网络数据集（包括 Telegram 和 irBlogs）上评估了 DeepLink 的性能。在这两个数据集上，所提出的框架优于链接预测问题的几种结构化和混合方法。