期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：1
页码：485
DOI：10.15680/IJIRCCE.2017.0501098
出版社：S&S Publications
摘要：Many types of data can be represented as graphs such as documentary data, Chemical molecularstructures and images. There is one issue in these graphs that they cannot find the hidden data topics. Topic model cansolve this problem successfully. To address this problem a Graph Topic Model (GTM) can be used. A Bernoullidistribution may used to model edges between nodes in the graph. It will make edges in graph to find latent topicdiscovery and then accuracy of supervised and unsupervised learning can be improved.
关键词：Graph mining; Latent Dirichlet Allocation (LDA); Topic model
