{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:36:48Z","timestamp":1769830608934,"version":"3.49.0"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,6,29]],"date-time":"2017-06-29T00:00:00Z","timestamp":1498694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-1247581"],"award-info":[{"award-number":["IIS-1247581"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01-CA180776"],"award-info":[{"award-number":["R01-CA180776"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Two Sigma Investments, LP"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2017,11,30]]},"abstract":"<jats:p>\n            <jats:italic>\u201cOgni lassada xe persa.\u201d<\/jats:italic>\n            <jats:sup>1<\/jats:sup>\n            -- Proverb from Trieste, Italy.\n          <\/jats:p>\n          <jats:p>\n            We present\n            <jats:sc>tri\u00e8st<\/jats:sc>\n            , a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully dynamic graph represented as an adversarial stream of edge insertions and deletions.\n          <\/jats:p>\n          <jats:p>Our algorithms use reservoir sampling and its variants to exploit the user-specified memory space at all times. This is in contrast with previous approaches, which require hard-to-choose parameters (e.g., a fixed sampling probability) and offer no guarantees on the amount of memory they use. We analyze the variance of the estimations and show novel concentration bounds for these quantities.<\/jats:p>\n          <jats:p>\n            Our experimental results on very large graphs demonstrate that\n            <jats:sc>tri\u00e8st<\/jats:sc>\n            outperforms state-of-the-art approaches in accuracy and exhibits a small update time.\n          <\/jats:p>","DOI":"10.1145\/3059194","type":"journal-article","created":{"date-parts":[[2017,6,30]],"date-time":"2017-06-30T12:36:19Z","timestamp":1498826179000},"page":"1-50","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":67,"title":["TRI\u00c8ST"],"prefix":"10.1145","volume":"11","author":[{"given":"Lorenzo De","family":"Stefani","sequence":"first","affiliation":[{"name":"Brown University, Providence, RI"}]},{"given":"Alessandro","family":"Epasto","sequence":"additional","affiliation":[{"name":"Google Inc., New York, NY"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2523-4420","authenticated-orcid":false,"given":"Matteo","family":"Riondato","sequence":"additional","affiliation":[{"name":"Two Sigma Investments LP, Avenue of the Americas, New York"}]},{"given":"Eli","family":"Upfal","sequence":"additional","affiliation":[{"name":"Brown University, Providence, RI"}]}],"member":"320","published-online":{"date-parts":[[2017,6,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623757"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA\u201902)","author":"Bar-Yossef Ziv"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1839490.1839494"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.056119"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963488"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00453-015-0036-4"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1142351.1142388"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2318857.2254798"},{"key":"e_1_2_1_10_1","volume-title":"http:\/\/webscope.sandbox.yahoo.com. 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Last.fm song network dataset. Retrieved from http:\/\/konect.uni-koblenz.de\/networks\/lastfm_song.  The Koblenz Network Collection (KONECT). 2016. Last.fm song network dataset. 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