  Graph is a ubiquitous data structure in data science that is widely applied
in social networks, knowledge representation graphs, recommendation systems,
etc. When given a graph dataset consisting of one graph or more graphs, where
the graphs are weighted in general, the first step is often to find clusters in
the graphs. In this paper, we present some ongoing research results on graph
clustering algorithms for clustering weighted graph datasets, which we name as
Weighted Graph Node Clustering via Gumbel Softmax (WGCGS for short). We apply
WGCGS on the Karate club weighted network dataset. Our experiments demonstrate
that WGCGS can efficiently and effectively find clusters in the Karate club
weighted network dataset. Our algorithm's effectiveness is demonstrated by (1)
comparing the clustering result obtained from our algorithm and the given
labels of the dataset; and (2) comparing various metrics between our clustering
algorithm and other state-of-the-art graph clustering algorithms.
