Yingqiu Zhu, Danyang Huang, Wei Xu, Bo Zhang 

【Publication Time】2020.03.16

【Lead Author】Yingqiu Zhu

【Corresponding Author】Danyang Huang

【Journal】 JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE

【Abstract】

Link prediction is one of the most important personalized services in social network platforms. The key point is to predict the probability of the existence of a link between two nodes based on various information in the network. This article combines information of the network structure with the user-generated contents. We propose link prediction indices based on both network structure and topic distribution (NSTD). In contrast to previous literatures, this approach makes full use of the network characteristics, such as homophily, transitivity, clustering, and degree heterogeneity. And we combine these characteristics with topic similarity when constructing indices based on both directly and indirectly connected nodes. Experiment results demonstrate that the proposed method outperforms the previous methods.

【Keywords】

Link prediction, social network analysis, topic models, data mining, directed network