Link prediction combining network structure and topic distribution in large-scale directed network

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

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

Danyang Huang’s research was supported by the National Natural Science Foundation of China (Grant No. 11701560), and the Center for Applied Statistics, School of Statistics, Renmin University of China. Wei Xu’s research was supported by the National Natural Science Foundation of China (Grant No. 71771212, U1711262). Bo Zhang’s research was supported by the National Natural Science Foundation of China (Grant No. 71873137).

Notes on contributors

Yingqiu Zhu

Yingqiu Zhu, Ph.D. student, Renmin University of China, Beijing, P.R. China. Research Direction: Social Network Analysis, Business Intelligence. Email: [email protected].

Danyang Huang

Danyang Huang, Associate Professor, Renmin University of China, Beijing, P.R. China. Research Direction: Social Network Analysis, High Dimensional Data Analysis. Email: [email protected].

Wei Xu

Wei Xu, Associate Professor, Renmin University of China, Beijing, P.R. China. Research Direction: Information System, Social Network Analysis. Email: [email protected].

Bo Zhang

Bo Zhang, Professor, Renmin University of China, Beijing, P.R. China. Research Direction: Mathematical Statistics, High Frequency Finance. Email: [email protected].