Managing and Visualizing Citation Network Using Graph Database and LDA Model (original) (raw)

Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017

Abstract

In this paper, a solution of storing and inferring on citation network using graph database and topic model is proposed. Citation network is a very large directed graph containing nodes and edges. Each node is a paper and each directed edge is a link between paper and its citing papers. In citation network analysis, each node usually contains basic properties of paper such as paper ID, publication year, paper title, authors. In this research, we propose one more property called "topic vector". This property contains topic distribution of a specific paper which is gained by LDA algorithm. After that, we propose a new approach to store and manage the citation network using graph database. Finally, we use the graph query language to develop some functions of citation network analysis and visualize topic propagation through the network. We also compare our approach with the traditional method in which the relational database is used to store and manage citation network. Experimental results show that the performance of our approach is higher than the traditional one and a different view of citation network analysis is also discussed.

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