Improving the DeepWalk Algorithm for Link Prediction In Social Networks

The increasing growth of social networks has drawn researchers' attention to link prediction, and it has been used in many fields, including computer science, information science, and anthropology. One of the newest link prediction methods is graph embedding methods, which are used to generate a feature vector for each node of the graph and find unknown links. The DeepWalk algorithm is one of the most popular graph embedding methods that captures the network structure using a random walk with equal probability. In this paper, a modified version of the DeepWalk algorithm is proposed, which uses a new random walk model to solve the link prediction problem. In fact, in the proposed method, the amount of structural similarity and the similarity of important features of nodes are combined. The results show that two nodes are more likely to form a link if they have similar structure and important features. To evaluate the proposed method, experiments have been conducted on five datasets. The test results indicate a relative improvement in the results obtained.

Keywords:

Social network analysis , Link prediction , Graph embedding , Representation learning , Random walk