关键词: 链接预测, 社会网络, 随机游走, 网络结构, 节点属性

Abstract: Link prediction, which aims at recommending potential links between network nodes, is an important step to understand and study the characteristics of social networks. With the development of social networks, many networks contain rich node attributes. This paper focuses on using both network structure and node attributes to predict links. Based on the assumption that two nodes in the network may be connected because they are close in the network, or may be linked for they have similar attributes, a new random walk model for link prediction by combining network structure and node attributes is proposed. First, two different graphs and transition matrices are created for new iteration rule. Second, the model is simplified for calculation and then a fast approximation algorithm is presented. The experiment on two standard datasets reveals that this method has better performance compared with other similar methods. Meanwhile, the effect of the probability of particle walking on different graphs is analyzed and it shows that node attributes can promote the prediction ability effectively.

Key words: link prediction, social network, random walk, network structure, node attribute