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dc.contributor.authorWang, Pinghui
dc.contributor.authorZhao, Junzhou
dc.contributor.authorRibeiro, Bruno
dc.contributor.authorLui, John C. S.
dc.contributor.authorTowsley, Don
dc.contributor.authorGuan, Xiaohong
dc.date.accessioned2018-03-11T06:54:14Z
dc.date.available2018-03-11T06:54:14Z
dc.date.issued2018-02-14
dc.identifier.citationWang P, Zhao J, Ribeiro B, Lui JCS, Towsley D, et al. (2018) Practical characterization of large networks using neighborhood information. Knowledge and Information Systems. Available: http://dx.doi.org/10.1007/s10115-018-1167-0.
dc.identifier.issn0219-1377
dc.identifier.issn0219-3116
dc.identifier.doi10.1007/s10115-018-1167-0
dc.identifier.urihttp://hdl.handle.net/10754/627275
dc.description.abstractCharacterizing large complex networks such as online social networks through node querying is a challenging task. Network service providers often impose severe constraints on the query rate, hence limiting the sample size to a small fraction of the total network of interest. Various ad hoc subgraph sampling methods have been proposed, but many of them give biased estimates and no theoretical basis on the accuracy. In this work, we focus on developing sampling methods for large networks where querying a node also reveals partial structural information about its neighbors. Our methods are optimized for NoSQL graph databases (if the database can be accessed directly), or utilize Web APIs available on most major large networks for graph sampling. We show that our sampling method has provable convergence guarantees on being an unbiased estimator, and it is more accurate than state-of-the-art methods. We also explore methods to uncover shortest paths between a subset of nodes and detect high degree nodes by sampling only a small fraction of the network of interest. Our results demonstrate that utilizing neighborhood information yields methods that are two orders of magnitude faster than state-of-the-art methods.
dc.description.sponsorshipThe authors wish to thank the anonymous reviewers for their helpful feedback. This work was supported in part by Army Research Office Contract W911NF-12-1-0385, and ARL under Cooperative Agreement W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied of the ARL, or the U.S. Government. The work was also supported in part by National Natural Science Foundation of China (61603290, 61602371, U1301254), Ministry of Education & China Mobile Research Fund (MCM20160311), China Postdoctoral Science Foundation (2015M582663), Natural Science Basic Research Plan in Zhejiang Province of China (LGG18F020016), Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6034, 2017JM6095), Shenzhen Basic Research Grant (JCYJ20160229195940462).
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s10115-018-1167-0
dc.subjectCrawling
dc.subjectGraph sampling
dc.subjectOnline social network
dc.subjectRandom walk
dc.titlePractical characterization of large networks using neighborhood information
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalKnowledge and Information Systems
dc.contributor.institutionShenzhen Research Institute of Xi’an Jiaotong University, Shenzhen, China
dc.contributor.institutionMOE Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China
dc.contributor.institutionSchool of Computer Science, Purdue University, West Lafayette, USA
dc.contributor.institutionDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong
dc.contributor.institutionDepartment of Computer Science, University of Massachusetts Amherst, Amherst, USA
dc.contributor.institutionCenter for Intelligent and Networked Systems, Tsinghua University, Beijing, China
dc.identifier.arxividarXiv:1311.3037
kaust.personZhao, Junzhou


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