{"type":"ScholarlyArticle","title":"Unified Representation and Lifted Sampling for Generative Models of Social Networks","authors":[{"name":"Pablo Robles-Granda","slug":"pablo-robles-granda","given":"Pablo","family":"Robles-Granda","affiliation":"Department of Computer Science , Purdue University"},{"name":"Sebastian Moreno","slug":"sebastian-moreno","given":"Sebastian","family":"Moreno","affiliation":"Faculty of Engineering and Science , Universidad Adolfo Ibañez 1"},{"name":"Jennifer Neville","slug":"jennifer-neville","given":"Jennifer","family":"Neville","affiliation":"Department of Computer Science , Purdue University"}],"abstract":"Statistical models of network structure are widely used in network science to reason about the properties of complex systems—where the nodes and edges represent entities and their relationships. Recently, a number of generative network models (GNM) have been developed that accurately capture characteristics of real world networks, but since they are typically defined in a procedural manner, it is difficult to identify commonalities in their structure. Moreover, procedural definitions make it difficult to develop statistical sampling algorithms that are both efficient and correct. In this paper, we identify a family of GNMs that share a common latent structure and create a Bayesian network (BN) representation that captures their common form. We show how to reduce two existing GNMs to this representation. Then, using the BN representation we develop a generalized, efficient, and provably correct, sampling method that exploits parametric symmetries and deterministic context-specific dependence. Finally, we use the new representation to design a novel GNM and evaluate it empirically.","doi":"10.24963/ijcai.2017/531","url":"https://ijcai.org/proceedings/2017/531","pdf":"https://ijcai.org/proceedings/2017/0531.pdf","markdown":"https://ijcai.org/proceedings/2017/531.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/531","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":531,"pages":{"first":3798,"last":3806},"keywords":["Multidisciplinary Topics and Applications: AI and Social Sciences","Knowledge Representation, Reasoning, and Logic: Logics for Knowledge Representation"],"videos":[],"references":[{"position":1,"raw":"[Benson et al., 2014] Austin R. 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