Semi-Supervised Label Propagation Community Detection on Graphs with Graph Neural Network

Abstract

The Graph Neural Network is a fairly innovative concept which permits neural networks to function on random graphs. As unbounded problem structures are universal in real-world scenarios and can be best denoted by graphs, Graph Neural Networks suggests new exhilarating applications and further simplified latent for machine learning wholly, but also noteworthy enhancement of performance in a deep learning domain. Graph Neural Networks are variant of Graph convolution networks can function sprightly on graphs. One of the well-known tasks attempted with this new skill is graph partitioning. Important characteristic of community is to discover graph nodes are with same interests and keep them strongly connected to extract groups for numerous reasons. We demonstrate a semi-supervised learning on graph data for solving community detection. In a number of trials on graph partitions we proved that our framework outperforms traditional ones.