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dc.contributor.authorWang, Pinghui
dc.contributor.authorQi, Yiyan
dc.contributor.authorSun, Yu
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorTao, Jing
dc.contributor.authorGuan, Xiaohong
dc.date.accessioned2021-04-11T12:45:36Z
dc.date.available2021-04-11T12:45:36Z
dc.date.issued2017-10
dc.identifier.citationWang, P., Qi, Y., Sun, Y., Zhang, X., Tao, J., & Guan, X. (2017). Approximately counting triangles in large graph streams including edge duplicates with a fixed memory usage. Proceedings of the VLDB Endowment, 11(2), 162–175. doi:10.14778/3149193.3149197
dc.identifier.issn2150-8097
dc.identifier.doi10.14778/3149193.3149197
dc.identifier.urihttp://hdl.handle.net/10754/668645
dc.description.abstractCounting triangles in a large graph is important for detecting network anomalies such as spam web pages and suspicious accounts (e.g., fraudsters and advertisers) on online social networks. However, it is challenging to compute the number of triangles in a large graph represented as a stream of edges with a low computational cost when given a limited memory. Recently, several effective sampling-based approximation methods have been developed to solve this problem. However, they assume the graph stream of interest contains no duplicate edges, which does not hold in many real-world graph streams (e.g., phone calling networks). In this paper, we observe that these methods exhibit a large estimation error or computational cost even when modified to deal with duplicate edges using deduplication techniques such as Bloom filter and hash-based sampling. To solve this challenge, we design a one-pass streaming algorithm for uniformly sampling distinct edges at a high speed. Compared to state-of-the-art algorithms, our algorithm reduces the sampling cost per edge from O(log k) (k is the maximum number of sampled edges determined by the available memory space) to O(1) without using any additional memory space. Based on sampled edges, we develop a simple yet accurate method to infer the number of triangles in the original graph stream. We conduct extensive experiments on a variety of real-world large graphs, and the results demonstrate that our method is several times more accurate and faster than state-of-the-art methods with the same memory usage.
dc.description.sponsorshipThe research presented in this paper is supported in part by National Natural Science Foundation of China (U1301254, 61603290, 61602371), 111 International Collaboration Program of China, Ministry of Education&China Mobile Research Fund (MCM20160311), Natural Science Foundation of Jiangsu Province (SBK2014021758), Prospective Joint Research of Industry-Academia-Research Joint Innovation Funding of Jiangsu Province (BY2014074), Shenzhen Basic Research Grant (JCYJ20160229195940462), China Postdoctoral Science Foundation (2015M582663), Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ6034).
dc.publisherVLDB Endowment
dc.relation.urlhttps://dl.acm.org/doi/10.14778/3149193.3149197
dc.rightsArchived with thanks to Proceedings of the VLDB Endowment
dc.titleApproximately counting triangles in large graph streams including edge duplicates with a fixed memory usage
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.identifier.journalProceedings of the VLDB Endowment
dc.eprint.versionPost-print
dc.contributor.institutionXi'an Jiaotong University, China and Shenzhen Research Institute of Xi'an Jiaotong University, Shenzhen, China
dc.contributor.institutionXi'an Jiaotong University, China
dc.contributor.institutionXi'an Jiaotong University, China and Tsinghua University, Beijing, China
dc.identifier.volume11
dc.identifier.issue2
dc.identifier.pages162-175
kaust.personZhang, Xiangliang
dc.identifier.eid2-s2.0-85055709648


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