{"type":"ScholarlyArticle","title":"Improving Stochastic Block Models by Incorporating Power-Law Degree Characteristic","authors":[{"name":"Maoying Qiao","slug":"maoying-qiao","given":"Maoying","family":"Qiao","affiliation":"School of Computer Science , Hangzhou Dianzi University"},{"name":"Jun Yu","slug":"jun-yu","given":"Jun","family":"Yu","affiliation":"School of Computer Science , Hangzhou Dianzi University"},{"name":"Wei Bian","slug":"wei-bian","given":"Wei","family":"Bian","affiliation":"School of Software , FEIT , The University of Technology Sydney"},{"name":"Qiang Li","slug":"qiang-li","given":"Qiang","family":"Li","affiliation":"School of Software , FEIT , The University of Technology Sydney; Department of Computing , The Hong Kong Polytechnic University"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"UBTECH Sydney Artificial Intelligence Institute , School of IT , FEIT , The University of Sydney"}],"abstract":"Stochastic block models (SBMs) provide a statistical way modeling network data, especially in representing clusters or community structures. However, most block models do not consider complex characteristics of networks such as scale-free feature, making them incapable of handling degree variation of vertices, which is ubiquitous in real networks. To address this issue, we introduce degree decay variables into SBM, termed power-law degree SBM (PLD-SBM), to model the varying probability of connections between node pairs. The scale-free feature is approximated by a power-law degree characteristic. Such a property allows PLD-SBM to correct the distortion of degree distribution in SBM, and thus improves the performance of cluster prediction. Experiments on both simulated networks and two real-world networks including the Adolescent Health Data and the political blogs network demonstrate the validity of the motivation of PLD-SBM, and its practical superiority.","doi":"10.24963/ijcai.2017/365","url":"https://ijcai.org/proceedings/2017/365","pdf":"https://ijcai.org/proceedings/2017/0365.pdf","markdown":"https://ijcai.org/proceedings/2017/365.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/365","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":"Machine Learning A-R","track":"Main track","number":365,"pages":{"first":2620,"last":2626},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Data Mining","Machine Learning: Learning Graphical Models"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Glance, 2005] L. 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