GraphBTM is a topic model which is an unsupervised algorithm to understand documents. It learns to discover the latent representation of documents and produce meaning clustering of words in the same topic. The goal of GraphBTM is to overcome the limitations of the Latent Dirichlet Allocation (LDA) which suffers from the data sparsity problem in short text and Biterm Topic Model (BTM) which claims an insufficient whole-corpus topic distribution.