  Recently, in many systems such as speech recognition and visual processing,
deep learning has been widely implemented. In this research, we are exploring
the possibility of using deep learning in community detection among the graph
datasets. Graphs have gained growing traction in different fields, including
social networks, information graphs, the recommender system, and also life
sciences. In this paper, we propose a method of community detection clustering
the nodes of various graph datasets. We cluster different category datasets
that belong to Affiliation networks, Animal networks, Human contact networks,
Human social networks, Miscellaneous networks. The deep learning role in
modeling the interaction between nodes in a network allows a revolution in the
field of science relevant to graph network analysis. In this paper, we extend
the gumbel softmax approach to graph network clustering. The experimental
findings on specific graph datasets reveal that the new approach outperforms
traditional clustering significantly, which strongly shows the efficacy of deep
learning in graph community detection clustering. We do a series of experiments
on our graph clustering algorithm, using various datasets: Zachary karate club,
Highland Tribe, Train bombing, American Revolution, Dolphins, Zebra,
Windsurfers, Les Mis\'erables, Political books.
