Enhanced Community Detection via Convolutional Neural Network: A Modified Approach Based on MRFasGCN Algorithm

Community detection is a very important research topic in the field of Social Network Analysis. Lots of researchers are working in this field due to its applications in various fields like medicine, social, Business, Marketing, and research. Researchers are proposing new algorithms to detect the communities having better performance as compared to the existing techniques. Initially, Newman and Girvan proposed traditional algorithms for community detection from social networks in 2004, but with the growth of social networks, Convolutional Neural Network (CNN) based algorithms are proposed by different researchers in recent years due to the inefficiency of traditional methods. After reviewing the state-of-the-art algorithms based on CNN learned that MRFasGCN is having the best performance compared to any other state-of-the-art algorithms for large data sets. In this algorithm, researchers have integrated the technique of Graph Convolutional Neural Network (GCN) with the statistical model Markov Random Field (MRF) to get better results and after implementing it on large datasets comparison is done on its results with other state-of-the-art algorithms and got to know its performance is far better than any other algorithm. While MRFasGCN is performing well on social networks and provides ground truth communities, there is a possibility available for improvement due to the sparsity problem. This paper proposes a new algorithm called Modified MRFasGCN. Two modifications are done to the existing algorithm, 1. In pre-processing, rather than passing the adjacency matrix with the normalized adjacency matrix, it will pass the reconstructed adjacency matrix and normalized reconstructed adjacency matrix, which resolves the sparsity problem, 2. GCN layer output will be fed to the MRF layer and refined results will be passed to the Adam optimizer without subtraction. Our experimental analysis shows that the modified algorithm provides better ground truth communities than the MRFasGCN and solves the problem of sparsity as passes a reconstructed adjacency matrix. In this paper, the proposed algorithm is executed on different datasets having different sizes like CORA (2708 Nodes), Flicker (80513 Nodes) and DBLP (317080 Nodes) and compared on different Community Detection metrics like accuracy, NMI, F1 Score, and execution time with other algorithms. After Experiments NMI value for MRFasGCN on DBLP data set is 0.662 while for Modified MRFasGCN it is 0.672, Modified MRFasGCN algorithm provides significant improvement of 2.9% in performance as compared to MRFasGCN. F1-Score of proposed algorithm is 0.511 on DBLP dataset which is better than MRFasGCN.