Weighted Graph Nodes Clustering via Gumbel Softmax
This work addresses graph clustering for weighted graphs, which is incremental as it applies a known technique (Gumbel Softmax) to a specific domain without broad impact.
The paper tackles the problem of clustering nodes in weighted graphs, presenting the Weighted Graph Node Clustering via Gumbel Softmax (WGCGS) algorithm, which is shown to efficiently and effectively find clusters in the Karate club weighted network dataset through comparisons with given labels and other state-of-the-art methods.
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.