{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-bayesian-model-for-sparse-graphs-with","title":"A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure","arxiv_id":"1810.01778","date":"2018-10-03","proceeding":null,"authors":["Juho Lee","Lancelot F. James","Seungjin Choi","François Caron"],"abstract":"We consider a non-projective class of inhomogeneous random graph models with\ninterpretable parameters and a number of interesting asymptotic properties.\nUsing the results of Bollob\\'as et al. [2007], we show that i) the class of\nmodels is sparse and ii) depending on the choice of the parameters, the model\nis either scale-free, with power-law exponent greater than 2, or with an\nasymptotic degree distribution which is power-law with exponential cut-off. We\npropose an extension of the model that can accommodate an overlapping community\nstructure. Scalable posterior inference can be performed due to the specific\nchoice of the link probability. We present experiments on five different\nreal-world networks with up to 100,000 nodes and edges, showing that the model\ncan provide a good fit to the degree distribution and recovers well the latent\ncommunity structure.","url_abs":"http://arxiv.org/abs/1810.01778v1","url_pdf":"http://arxiv.org/pdf/1810.01778v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-bayesian-model-for-sparse-graphs-with","repo_url":"https://github.com/OxCSML-BayesNP/BNRG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}