{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:47:37Z","timestamp":1753876057256,"version":"3.41.2"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T00:00:00Z","timestamp":1614729600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Science and Engineering Research Board, Department of Science and Technology, Government of India"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,3,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii) interpret the discovered communities by identifying the relative importance of different covariates in them. Recent methods have almost exclusively focused on the first point above. However, the quantitative improvements offered by them are often due to complex black-box models like deep neural networks at the expense of interpretability. Approaches that focus on the second point are either domain specific or have poor performance in practice. This article proposes interpretable, domain-independent statistical models for networks with node covariates that additionally offer good quantitative performance. The proposed models equip Stochastic Block Models with Restricted Boltzmann Machines to provide interpretable insights about the communities and they support both pure and mixed community memberships. Besides providing interpretability, our approach\u2019s main strength is that it does not explicitly assume a causal direction between community memberships and node covariates, making it applicable in diverse domains. We derive efficient inference procedures for our models, which can, in some cases, run in linear time in the number of nodes and edges. Experiments on several synthetic and real-world networks demonstrate that our models achieve close to state-of-the-art performance on community detection and link prediction tasks while also providing interpretations for the discovered communities.<\/jats:p>","DOI":"10.1093\/comnet\/cnac009","type":"journal-article","created":{"date-parts":[[2022,3,24]],"date-time":"2022-03-24T21:47:35Z","timestamp":1648158455000},"source":"Crossref","is-referenced-by-count":0,"title":["Equipping SBMs with RBMs: an interpretable approach for analysis of networks with covariates"],"prefix":"10.1093","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9692-4096","authenticated-orcid":false,"given":"Shubham","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Computer Science and Automation, Indian Institute of Science, Bangalore - 560012, India"}]},{"given":"Gururaj","family":"K","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Automation, Indian Institute of Science, Bangalore - 560012, India"}]},{"given":"Ambedkar","family":"Dukkipati","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Automation, Indian Institute of Science, Bangalore - 560012, India"}]},{"given":"Rui M","family":"Castro","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Eindhoven University of Technology and Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven 5600 MB, The Netherlands"}]}],"member":"286","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"2022041312513988700_B1","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/biomet\/asx008","article-title":"Covariate assisted spectral clustering","volume":"104","author":"Binkiewicz,","year":"2017","journal-title":"Biometrika"},{"key":"2022041312513988700_B2","first-page":"1151","article-title":"Community detection in networks with node attributes","author":"Yang,","year":"2013","journal-title":"Proceedings of the IEEE 13th International Conference on Data Mining"},{"key":"2022041312513988700_B3","article-title":"Variational graph auto-encoders","author":"Kipf,","year":"2016","journal-title":"NIPS Workshop on Bayesian Deep Learning"},{"key":"2022041312513988700_B4","first-page":"4466","article-title":"Stochastic blockmodels meet graph neural networks","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Mehta,","year":"2019"},{"key":"2022041312513988700_B5","first-page":"2609","article-title":"Adversarially regularized graph autoencoder for graph embedding","volume-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","author":"Pan,","year":"2018"},{"key":"2022041312513988700_B6","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/2623330.2623732","article-title":"DeepWalk: online learning of social representations","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Perozzi,","year":"2014"},{"key":"2022041312513988700_B7","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1145\/2488388.2488483","article-title":"Efficient community detection in large networks using content and links","volume-title":"Proceedings of the 22nd International Conference on World Wide Web","author":"Ruan,","year":"2013"},{"key":"2022041312513988700_B8","first-page":"3670","article-title":"Attributed graph clustering: a deep attentional embedding approach","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence","author":"Wang,","year":"2019"},{"key":"2022041312513988700_B9","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1137\/1.9781611972818.39","article-title":"Block-LDA: jointly modeling entity-annotated text and entity-entity links","author":"Balasubramanyan,","year":"2011","journal-title":"Proceedings of the 2011 SIAM International Conference on Data Mining"},{"key":"2022041312513988700_B10","first-page":"81","article-title":"Relational topic models for document networks","volume-title":"Proceedings of the 12th International Conference on Artificial Intelligence and Statistics","author":"Chang,","year":"2009"},{"key":"2022041312513988700_B11","first-page":"430","article-title":"The missing link - a probabilistic model of document content and hypertext connectivity","volume":"13","author":"Cohn,","year":"2001","journal-title":"Adv. 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