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Author (up) Moreno, S.; Neville, J.; Kirshner, S. pdf  doi
  Title Tied Kronecker Product Graph Models to Capture Variance in Network Populations Type Journal Article
  Year 2018 Publication Acm Transactions On Knowledge Discovery From Data Abbreviated Journal ACM Trans. Knowl. Discov. Data  
  Volume 12 Issue 3 Pages 40 pp  
  Keywords Graph generation models; social network analysis; kronecker product graph models  
  Abstract Much of the past work on mining and modeling networks has focused on understanding the observed propel ties of single example graphs. However, in many real-life applications it is important to characterize the structure of populations of graphs. In this work, we analyze the distributional properties of probabilistic generative graph models (PGGMs) for network populations. PGGMs are statistical methods that model the network distribution and match common characteristics of real-world networks. Specifically, we show that most PGGMs cannot relied the natural variability in graph properties observed across multiple networks because their edge generation process assumes independence among edges. Then, we propose the mixed Kronecker Product Graph Model (mKPGM) a scalable generalization of KPGMs that uses tied parameters to increase the variability of the sampled networks, while preserving the edge probabilities in expectation. We compare mKPGM to several other graph models. The results show that learned mKPGMs accurately represent the characteristics of real-world networks, while also effectively capturing the natural variability in network structure.  
  Address [Moreno, Sebastian] Univ Adolfo Ibanez, Avda Padre Hurtado 750,A-212, Vina Del Mar, Chile, Email:;  
  Corporate Author Thesis  
  Publisher Assoc Computing Machinery Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1556-4681 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000431790300010 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 857  
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