  Graph clustering is a fundamental and challenging task in the field of graph
mining where the objective is to group the nodes into clusters taking into
consideration the topology of the graph. It has several applications in diverse
domains spanning social network analysis, recommender systems, computer vision,
and bioinformatics. In this work, we propose a novel method, DGCluster, which
primarily optimizes the modularity objective using graph neural networks and
scales linearly with the graph size. Our method does not require the number of
clusters to be specified as a part of the input and can also leverage the
availability of auxiliary node level information. We extensively test DGCluster
on several real-world datasets of varying sizes, across multiple popular
cluster quality metrics. Our approach consistently outperforms the
state-of-the-art methods, demonstrating significant performance gains in almost
all settings.
