Community Detection Using Semi-Supervised Learning With Graph Convolutional Network On Gpus

Graph Convolutional Network (GCN) has drawn considerable research attention in recent times. Many different problems from diverse domains can be solved efficiently using GCN. Community detection in graphs is a computationally challenging graph analytic problem. The presence of only a limited amount of labelled data (known communities) motivates us for using a learning approach to community discovery. However, detecting communities in large graphs using semi-supervised learning with GCN is still an open problem due to the scalability and accuracy issues. In this paper, we present a scalable method for detecting communities based on GCN via semi-supervised node classification. We optimize the hyper-parameters for our semi-supervised model for detecting communities using PyTorch with CUDA on GPU environment. We apply Mini-batch Gradient Descent for larger datasets to resolve the memory issue. We demonstrate an experimental evaluation on different real-world networks from diverse domains. Our model achieves up to 86.9% accuracy and 0.85 F1 Score on these practical datasets. We also show that using identity matrix as features, based on the graph connectivity, performs better with higher accuracy than that of vertex-based graph features. We accelerate the model performance 4 times with the use of GPUs over CPUs.