In recent years, a number of random graph models, such as preferential attachment, have been proposed as probabilistic models of large graphs. We suggest an objective method for ranking their performance on actual graphs. In particular, we look at the probability that a model assigns to a given graph, and design efficient MCMC algorithms for estimating these quantities.<\/p>\n","protected":false},"excerpt":{"rendered":"
In recent years, a number of random graph models, such as preferential attachment, have been proposed as probabilistic models of large graphs. We suggest an objective method for ranking their performance on actual graphs. In particular, we look at the probability that a model assigns to a given graph, and design efficient MCMC algorithms for […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM Press","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"In Proceedings of the 23rd International Conference on Machine Learning (ICML)","msr_editors":"","msr_how_published":"","msr_isbn":"1-59593-383-2","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"105-112","msr_page_range_start":"105","msr_page_range_end":"112","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"In Proceedings of the 23rd International Conference on Machine Learning 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