{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:08:31Z","timestamp":1742936911906,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434174"},{"type":"electronic","value":"9783031434181"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43418-1_14","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T09:02:26Z","timestamp":1694854946000},"page":"226-241","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SimSky: An Accuracy-Aware Algorithm for\u00a0Single-Source SimRank Search"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3710-8840","authenticated-orcid":false,"given":"Liping","family":"Yan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-9475","authenticated-orcid":false,"given":"Weiren","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"issue":"11\u201312","key":"14_CR1","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1016\/j.apnum.2007.01.003","volume":"57","author":"C Bekas","year":"2007","unstructured":"Bekas, C., Kokiopoulou, E., Saad, Y.: An estimator for the diagonal of a matrix. Appl. Numer. Math. 57(11\u201312), 1214\u20131229 (2007)","journal-title":"Appl. Numer. Math."},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1007\/BF02523124","volume":"13","author":"DL Boley","year":"1994","unstructured":"Boley, D.L.: Krylov space methods on state-space control models. Circuits Syst. Signal Process. 13, 733\u2013758 (1994)","journal-title":"Circuits Syst. Signal Process."},{"issue":"3","key":"14_CR3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TKDE.2007.46","volume":"19","author":"F Fouss","year":"2007","unstructured":"Fouss, F., Pirotte, A., Renders, J.M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355\u2013369 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Fujiwara, Y., Nakatsuji, M., Shiokawa, H., Onizuka, M.: Efficient search algorithm for SimRank. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 589\u2013600. IEEE (2013)","DOI":"10.1109\/ICDE.2013.6544858"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538\u2013543 (2002)","DOI":"10.1145\/775047.775126"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Jeh, G., Widom, J.: Scaling personalized web search. In: Proceedings of the 12th International Conference on World Wide Web, pp. 271\u2013279 (2003)","DOI":"10.1145\/775189.775191"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Kusumoto, M., Maehara, T., Kawarabayashi, K.i.: Scalable similarity search for SimRank. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 325\u2013336 (2014)","DOI":"10.1145\/2588555.2610526"},{"issue":"7","key":"14_CR8","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1002\/asi.20591","volume":"58","author":"D Liben-Nowell","year":"2007","unstructured":"Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019\u20131031 (2007)","journal-title":"J. Am. Soc. Inform. Sci. Technol."},{"key":"14_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.12.046","volume":"556","author":"J Lu","year":"2021","unstructured":"Lu, J., Gong, Z., Yang, Y.: A matrix sampling approach for efficient SimRank computation. Inf. Sci. 556, 1\u201326 (2021)","journal-title":"Inf. Sci."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Rothe, S., Sch\u00fctze, H.: CoSimRank: a flexible & efficient graph-theoretic similarity measure. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 1392\u20131402 (2014)","DOI":"10.3115\/v1\/P14-1131"},{"issue":"1","key":"14_CR11","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1137\/0729014","volume":"29","author":"Y Saad","year":"1992","unstructured":"Saad, Y.: Analysis of some Krylov subspace approximations to the matrix exponential operator. SIAM J. Numer. Anal. 29(1), 209\u2013228 (1992)","journal-title":"SIAM J. Numer. Anal."},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Tian, B., Xiao, X.: SLING: a near-optimal index structure for SimRank. In: Proceedings of the 2016 International Conference on Management of Data, pp. 1859\u20131874 (2016)","DOI":"10.1145\/2882903.2915243"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Wang, H., Wei, Z., Yuan, Y., Du, X., Wen, J.R.: Exact single-source SimRank computation on large graphs. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 653\u2013663 (2020)","DOI":"10.1145\/3318464.3389781"},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Yu, W., Iranmanesh, S., Haldar, A., Zhang, M., Ferhatosmanoglu, H.: Rolesim*: scaling axiomatic role-based similarity ranking on large graphs. World Wide Web 25(2), 785\u2013829 (2022). https:\/\/doi.org\/10.1007\/s11280-021-00925-z","DOI":"10.1007\/s11280-021-00925-z"},{"issue":"3","key":"14_CR15","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s00778-018-0536-3","volume":"28","author":"W Yu","year":"2019","unstructured":"Yu, W., Lin, X., Zhang, W., Pei, J., McCann, J.A.: Simrank*: effective and scalable pairwise similarity search based on graph topology. VLDB J. 28(3), 401\u2013426 (2019)","journal-title":"VLDB J."},{"issue":"5","key":"14_CR16","doi-asserted-by":"publisher","first-page":"569","DOI":"10.14778\/2735479.2735489","volume":"8","author":"W Yu","year":"2015","unstructured":"Yu, W., McCann, J.A.: Efficient partial-pairs SimRank search on large networks. Proc. VLDB Endow. 8(5), 569\u2013580 (2015)","journal-title":"Proc. VLDB Endow."},{"key":"14_CR17","doi-asserted-by":"publisher","unstructured":"Yu, W., McCann, J.A., Zhang, C., Ferhatosmanoglu, H.: Scaling high-quality pairwise link-based similarity retrieval on billion-edge graphs. ACM Trans. Inf. Syst. 40(4), 78:1\u201378:45 (2022). https:\/\/doi.org\/10.1145\/3495209","DOI":"10.1145\/3495209"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Yu, W., McCann, J.A.: High quality graph-based similarity search. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 83\u201392 (2015)","DOI":"10.1145\/2766462.2767720"},{"key":"14_CR19","doi-asserted-by":"publisher","unstructured":"Yu, W., Wang, F.: Fast exact CoSimRank search on evolving and static graphs. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, 23\u201327 April 2018, pp. 599\u2013608. ACM (2018). https:\/\/doi.org\/10.1145\/3178876.3186126","DOI":"10.1145\/3178876.3186126"},{"key":"14_CR20","doi-asserted-by":"publisher","unstructured":"Yu, W., Yang, J., Zhang, M., Wu, D.: CoSimHeat: an effective heat kernel similarity measure based on billion-scale network topology. In: WWW 2022: The ACM Web Conference 2022, Virtual Event, Lyon, France, 25\u201329 April 2022, pp. 234\u2013245. ACM (2022). https:\/\/doi.org\/10.1145\/3485447.3511952","DOI":"10.1145\/3485447.3511952"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43418-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:06:29Z","timestamp":1719407189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43418-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434174","9783031434181"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43418-1_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"We acknowledge the importance of ethical considerations in the design of our ApproxDiag and SimSky algorithms. All the datasets used in this paper are publicly-available online, and do not have any privacy issues. We ensure that our algorithms do not lead to any potential negative influences. We declare that we allow our algorithms to be used for the benefit of society.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"196","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.63","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}