A modified DeepWalk method for link prediction in attributed social network

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.