2021/02/22 by Deepak Bhaskar Acharya, Acharya, Deepak Bhaskar, Huaming Zhang +1
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Peer-to-Peer Network Technologies #Social and Information Networks (cs.SI) #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2102.10775
arxiv created 2021/02/22 · openalex publication_date 2021/02/22 · arxiv updated 2021/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.