Description

Graph mining is a special case of structured data mining. Many unstructured and structured data can be represented as graphs. Because of Graph mining ‘s numerous application scenarios it has been a popular research area . Although it has been a popular research area, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data. An innovative graph topic model (GTM) is proposed in this project. This model addresses the issues better than existing system. It uses Bernoulli distributions to model the edges between nodes in a graph. It can improve the accuracy of the supervised and unsupervised learning of graphs. the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics. An innovative GTM for graph-structured data is built by modeling the edges in graphs using Bernoulli distribution, which makes the edges in graphs contribute to the discovered topics. And then it uses two Inference algorithm. They are Variational algorithm and Markov chain Monte Carlo (MCMC) algorithm. These are developed to resolve the graph topic model.