Weighted Graph Nodes Clustering via Gumbel Softmax

• URL: http://arxiv.org/abs/2102.10775v1
• Date: Mon, 22 Feb 2021 05:05:35 GMT
• Title: Weighted Graph Nodes Clustering via Gumbel Softmax
• Authors: Deepak Bhaskar Acharya, Huaming Zhang
• Abstract summary: We present some ongoing research results on graph clustering algorithms for clustering weighted graph datasets. We name our algorithm as Weighted Graph Node Clustering via Gumbel Softmax (WGCGS)
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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