MOSS-5: A Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs (Extended Abstract)
Lui, John C.S.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-10-25
Print Publication Date2018-04
Permanent link to this recordhttp://hdl.handle.net/10754/630322
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AbstractDespite recent efforts in counting 3-node and 4-node graphlets, little attention has been paid to characterizing 5-node graphlets. In this paper, we develop a computationally efficient sampling method to estimate 5-node graphlet counts. We not only provide a fast sampling method and unbiased estimators of graphlet counts, but also derive simple yet exact formulas for the variances of the estimators which are of great value in practice-the variances can be used to bound the estimates' errors and determine the smallest necessary sampling budget for a desired accuracy. We conduct experiments on a variety of real-world datasets, and the results show that our method is several orders of magnitude faster than the state-of-The-Art methods with the same accuracy.
CitationWang P, Zhao J, Zhang X, Li Z, Cheng J, et al. (2018) MOSS-5: A Fast Method of Approximating Counts of 5-Node Graphlets in Large Graphs (Extended Abstract). 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00244.
Conference/Event name34th IEEE International Conference on Data Engineering, ICDE 2018