A community detection model using node embedding approach and graph convolutional network with clustering technique

The communities in a network have distinct characteristics and interrelationships. Community discovery methods based on node embedding and deep learning have surpassed spectral clustering and statistical inference as the preferred methods for handling high-dimensional network data. Community detection in graph networks using graph neural networks (GNNs) is a rapidly expanding field of study that has attracted much attention recently. Inspired by the graph convolutional network (GCN) approach, promising results in evaluating graph structure data, a different method for discovering communities using node embedding using Node2Vec via graph convolutional networks (CD2NE-GCN) model has been proposed. It has been evaluated against the most recent standard methods of graph attention network (GAT), GCN-based approach for Unsupervised Community Detection (GUCD), and Graph Adversarial Networks (GAN). In this paper, a technique for community detection that combines the ideas of graph neural network design with node embedding via message forwarding has been presented. The experimentation has made use of five benchmark datasets: Karate, Cora, CiteSeer, Facebook, and DBLP. In the first set of trials, the node2vec uses the skip-gram with negative sampling (SGNS) approach via biased random walks of the second order to produce node embeddings and feed them to the graph convolutional network. The weighted average matrix and scaled normalized matrices of low-dimensional vertices have been calculated. In the next set of experiments, the average weighted matrix was sent to the 3-layer GCN with the activation function for generating feature vectors and applied clustering technique for predicting communities. The CD2NE-GCN approach outperforms previous literature by detecting graph network communities with accuracy scores of 98.45%, 99.04%, 96.39%, 98.79%, and 97.67% for Karate, Cora, CiteSeer, Facebook, and DBLP datasets, respectively. Thus, graph convolutional network models with a node embedding approach enhance the accuracy.