{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:47:42Z","timestamp":1754156862656,"version":"3.41.2"},"reference-count":77,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2023,10,20]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, <jats:italic>F<\/jats:italic>1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7\u201387.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-02-2022-0056","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T01:04:08Z","timestamp":1675731848000},"page":"580-604","source":"Crossref","is-referenced-by-count":7,"title":["CommunityGCN: community detection using node classification with graph convolution network"],"prefix":"10.1108","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9265-5746","authenticated-orcid":false,"given":"Riju","family":"Bhattacharya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5306-5818","authenticated-orcid":false,"given":"Naresh Kumar","family":"Nagwani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0873-2102","authenticated-orcid":false,"given":"Sarsij","family":"Tripathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"issue":"8","key":"key2023102013125540000_ref001","first-page":"7","article-title":"TempNodeEmb:temporal node embedding considering temporal edge influence matrix","volume":"14","year":"2020"},{"issue":"4","key":"key2023102013125540000_ref002","first-page":"2097","article-title":"Pseudo-likelihood methods for community detection in large sparse networks","volume":"41","year":"2013","journal-title":"Annals of Statistics"},{"first-page":"361","article-title":"Gephi: an open source software for exploring and manipulating networks visualization and exploration of large graphs","year":"2009","key":"key2023102013125540000_ref003"},{"first-page":"1400","article-title":"Structural deep clustering network","year":"2020","key":"key2023102013125540000_ref004"},{"first-page":"1","article-title":"Spectral networks and deep locally connected networks on graphs","year":"2014","key":"key2023102013125540000_ref005"},{"issue":"9","key":"key2023102013125540000_ref006","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: problems, techniques, and applications","volume":"30","year":"2018","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"first-page":"3438","article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","year":"2020","key":"key2023102013125540000_ref007"},{"key":"key2023102013125540000_ref008","unstructured":"Costa, A.R. 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