The increasing growth of online social networks has drawn researchers' attention to link prediction and has been adopted in many fields, including computer sciences, information science, and anthropology. The link prediction in attributed networks is a new challenge in this field, one of the interesting topics in recent years. Nodes are also accompanied in many real-world systems by various attributes or features, known as attributed networks. One of the newest methods of link prediction is embedding methods to generate the feature vector of each node of the graph and find unknown connections. The DeepWalk algorithm is one of the most popular graph embedding methods that capture the network structure using pure random walking. The present paper seeks to present a modified version of deep walk based on pure random walking for solving link prediction in the attributed network, which will be used for both network structure and node attributes, and the new random walk model for link prediction will be introduced by integrating network structure and node attributes, based on the assumption that two nodes on the network will be linked since they are nearby in the network, or connected for the reason of similar attributes. The results indicate that two nodes are more probable to establish a link in the case of possessing more structure and attribute similarity. In order to justify the proposal, the authors carry out many experiments on six real-world attributed networks for comparison with the state-of-the-art network embedding methods. The experimental results from the graphs indicate that our proposed approach is more capable compared to other link prediction approaches and increases the accuracy of prediction.

在线社交网络的日益增长引起了研究人员对链接预测的关注，并已被许多领域采用，包括计算机科学、信息科学和人类学。属性网络中的链接预测是该领域的新挑战，也是近年来的热门话题之一。在许多现实世界系统中，节点还伴随着各种属性或特征，称为属性网络。链接预测的最新方法之一是嵌入方法来生成图的每个节点的特征向量并找到未知的连接。DeepWalk 算法是最流行的图嵌入方法之一，它使用纯随机游走来捕获网络结构。本文试图提出一种基于纯随机游走的深度游走的修改版本，用于解决属性网络中的链路预测，该版本将用于网络结构和节点属性，并将引入用于链路预测的新随机游走模型通过整合网络结构和节点属性，假设网络上的两个节点会因为它们在网络中靠近而连接，或者因为属性相似而连接。结果表明，两个节点在拥有更多结构和属性相似度的情况下更有可能建立链接。为了证明该提议的合理性，作者对六个真实世界的属性网络进行了许多实验，以与最先进的网络嵌入方法进行比较。