{"type":"ScholarlyArticle","title":"Overlapping Communities and Roles in Networks with Node Attributes: Probabilistic Graphical Modeling, Bayesian Formulation and Variational Inference (Extended Abstract)","authors":[{"name":"Gianni Costa","slug":"gianni-costa","given":"Gianni","family":"Costa","affiliation":"ICAR-CNR"},{"name":"Riccardo Ortale","slug":"riccardo-ortale","given":"Riccardo","family":"Ortale","affiliation":"ICAR-CNR"}],"abstract":"We study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm.\r\n\t\t\r\nA wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services.","doi":"10.24963/ijcai.2022/796","url":"https://ijcai.org/proceedings/2022/796","pdf":"https://ijcai.org/proceedings/2022/0796.pdf","markdown":"https://ijcai.org/proceedings/2022/796.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/796","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Journal Track","track":"Journal Track","number":796,"pages":{"first":5713,"last":5717},"keywords":["Machine Learning: Probabilistic Machine Learning","Machine Learning: General","Data Mining: Mining Graphs","Data Mining: General"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987237","length_sec":79,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985380","length_sec":652,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Anderson et al., 1992] Carolyn J. Anderson, Stanley Was- serman, and Katherine Faust. Building stochastic block- models. Social Networks, 14(1-2):137 -161, 1992.","title":"Stanley Wasserman, and Katherine Faust","authors":"Carolyn J Anderson","year":"1992","venue":"Social Networks","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arabie et al., 1978] Phipps Arabie, Scott A. Boorman, and Paul R Levitt. Constructing blockmodels: How and why. Journal of Mathematical Psychology, 17(1):21 -63, 1978.","title":"Constructing blockmodels: How and why","authors":"Scott A Phipps Arabie, Paul R Boorman, Levitt","year":"1978","venue":"Journal of Mathematical Psychology","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bishop, 2006] Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.","title":"Pattern Recognition and Machine Learning","authors":"Christopher M Bishop","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bishop, 2013] Christopher M. Bishop. Model-based ma- chine learning. Philosophical Transactions of the Royal Society A, page 371:20120222, 2013.","title":"Model-based machine learning","authors":"Christopher M Bishop","year":"2013","venue":"Philosophical Transactions of the Royal Society A","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Blei et al., 2017] David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. Variational inference: A review for sta- tisticians. Journal of the American Statistical Association, 112(518):859 -877, 2017.","title":"Variational inference: A review for statisticians","authors":"David M Blei, Alp Kucukelbir, Jon D Mcauliffe","year":"2017","venue":"Journal of the American Statistical Association","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Costa and Ortale, 2022] Gianni Costa and Riccardo Ortale. Overlapping communities and roles in networks with node attributes: Probabilistic graphical modeling, bayesian for- mulation and variational inference. Artificial Intelligence, 302: 103580, 2022.","title":"Overlapping communities and roles in networks with node attributes: Probabilistic graphical modeling, bayesian formulation and variational inference","authors":"Gianni Costa, Riccardo Ortale","year":"2022","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ghahramani, 2015] Zoubin Ghahramani. Probabilistic machine learning and artificial intelligence. Nature, 521(7553):452 -459, 2015.","title":"Probabilistic machine learning and artificial intelligence","authors":"Zoubin Ghahramani","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gopalan et al., 2015] Prem Gopalan, Jake M. Hofman, and David M. Blei. Scalable recommendation with hierarchi- cal poisson factorization. In UAI, pages 326 -335, 2015.","title":"Scalable recommendation with hierarchical poisson factorization","authors":"Prem Gopalan, Jake M Hofman, David M Blei","year":"2015","venue":"UAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Holland et al., 1983] Paul W. Holland, Kathryn B. Laskey, and Samuel Leinhardt. Stochastic blockmodels: First steps. Social Networks, 5(2):109 -137, 1983.","title":"Stochastic blockmodels: First steps","authors":"Paul W Holland, Kathryn B Laskey, Samuel Leinhardt","year":"1983","venue":"Social Networks","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jordan et al., 1999] Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul. An intro- duction to variational methods for graphical models. Ma- chine Learning, 37(2):183 -233, 1999.","title":"An introduction to variational methods for graphical models","authors":"I Michael, Zoubin Jordan, Tommi S Ghahramani, Lawrence K Jaakkola, Saul","year":"1999","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Koller and Friedman, 2009] Daphne Koller and Nir Fried- man. Probabilistic Graphical Models. Principles and Techniques. The MIT Press, 2009.","title":"Probabilistic Graphical Models. Principles and Techniques","authors":"Friedman Koller","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lattanzi and Sivakumar, 2009] Silvio Lattanzi and D. Siva- kumar. Affiliation networks. In ACM STOC, pages 427 - 434, 2009.","title":"Affiliation networks","authors":"Silvio Lattanzi, D Sivakumar","year":"2009","venue":"ACM STOC","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[MacKay, 2002] David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge Univer- sity Press, 2002.","title":"Information Theory, Inference, and Learning Algorithms","authors":"J C David, Mackay","year":"2002","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Murphy, 2012] Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, 2012.","title":"Machine Learning: A Probabilistic Perspective","authors":"Kevin P Murphy","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Pfeffer, 2016] Avi Pfeffer. Practical Probabilistic Program- ming. Manning Publications, 2016.","title":"Practical Probabilistic Programming","authors":"Avi Pfeffer","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Rossi and Ahmed, 2015] Ryan A. Rossi and Nesreen K. Ahmed. Role discovery in networks. IEEE Transactions on Knowledge and Data Engineering, 27(04):1112 -1131, 2015.","title":"Role discovery in networks","authors":"Ryan A Rossi, Nesreen K Ahmed","year":"2015","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Yang and Leskovec, 2013] Jaewon Yang and Jure Lesko- vec. Overlapping community detection at scale: A non- negative matrix factorization approach. In ACM WSDM, pages 587 -596, 2013.","title":"Overlapping community detection at scale: A nonnegative matrix factorization approach","authors":"Yang, Leskovec ;, Jaewon Yang, Jure Leskovec","year":"2013","venue":"ACM WSDM","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Yang et al., 2013] Jaewon Yang, Julian McAuley, and Jure Leskovec. Community detection in networks with node attributes. In ICDM, pages 1151 -1156, 2013.","title":"Community detection in networks with node attributes","authors":"Jaewon Yang, Julian Mcauley, Jure Leskovec","year":"2013","venue":"ICDM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}