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    SNOD: a fast sampling method of exploring node orbit degrees for large graphs

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    Type
    Article
    Authors
    Wang, Pinghui
    Zhao, Junzhou
    Zhang, Xiangliang cc
    Tao, Jing
    Guan, Xiaohong
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2018-12-13
    Permanent link to this record
    http://hdl.handle.net/10754/630298
    
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    Abstract
    Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing how frequent a graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little effort has been made to characterize a node’s graphlet orbit degree, i.e., the number of CISes isomorphic to the graphlet that touch the node at a particular orbit, which is an important fine-grained metric for analyzing complex networks such as learning functions/roles of nodes in social and biological networks. Like global graphlet counting, it is computationally intensive to compute node orbit degrees for a large graph. Furthermore, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper, we propose a novel sampling method SNOD to efficiently estimate node orbit degrees for large-scale graphs and quantify the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-world datasets and demonstrate that our method SNOD is several orders of magnitude faster than state-of-the-art enumeration methods for accurately estimating node orbit degrees for graphs with millions of edges.
    Citation
    Wang P, Zhao J, Zhang X, Tao J, Guan X (2018) SNOD: a fast sampling method of exploring node orbit degrees for large graphs. Knowledge and Information Systems. Available: http://dx.doi.org/10.1007/s10115-018-1301-z.
    Sponsors
    The authors wish to thank the anonymous reviewers for their helpful feedback. The research presented in this paper is supported in part by National Key R&D Program of China (2018YFC0830500), National Natural Science Foundation of China (U1301254, 61603290, 61602371), the Ministry of Education&China Mobile Research Fund (MCM20160311), 111 International Collaboration Program of China, China Postdoctoral Science Foundation (2015M582663), Shenzhen Basic Research Grant (JCYJ20160229195940462, JCYJ20170816100819428), Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6034).
    Publisher
    Springer Nature
    Journal
    Knowledge and Information Systems
    DOI
    10.1007/s10115-018-1301-z
    Additional Links
    http://link.springer.com/article/10.1007/s10115-018-1301-z
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10115-018-1301-z
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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