{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:30:46Z","timestamp":1742981446165,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031306747"},{"type":"electronic","value":"9783031306754"}],"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\/978-3-031-30675-4_21","type":"book-chapter","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T10:02:24Z","timestamp":1681466544000},"page":"308-319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning with\u00a0Small Data: Subgraph Counting Queries"],"prefix":"10.1007","author":[{"given":"Kangfei","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Jeffrey Xu","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zongyan","family":"He","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Rong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,15]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"S. A. Cook. The complexity of theorem-proving procedures. In: Proceedings of the STOC, pp. 151\u2013158 (1971)","key":"21_CR1","DOI":"10.1145\/800157.805047"},{"issue":"10","key":"21_CR2","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1109\/TPAMI.2004.75","volume":"26","author":"LP Cordella","year":"2004","unstructured":"Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1367\u20131372 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the NeurIPS 2017, pp. 1024\u20131034 (2017)","key":"21_CR3"},{"doi-asserted-by":"crossref","unstructured":"Liu, X., Pan, H., He, M., Song, Y., Jiang, X., Shang, L.: Neural subgraph isomorphism counting. In: Proceedings of the KDD 2020, pp. 1959\u20131969 (2020)","key":"21_CR4","DOI":"10.1145\/3394486.3403247"},{"doi-asserted-by":"crossref","unstructured":"Park, Y., Ko, S., Bhowmick, S.S., Kim, K., Hong, K., Han, W.: G-CARE: a framework for performance benchmarking of cardinality estimation techniques for subgraph matching. In: Proceedings of the SIGMOD 2020, pp. 1099\u20131114 (2020)","key":"21_CR5","DOI":"10.1145\/3318464.3389702"},{"unstructured":"Patacchiola, M., Turner, J., Crowley, E.J., Storkey, A.: Bayesian meta-learning for the few-shot setting via deep kernels. In: Proceedings of NeurIPS (2020)","key":"21_CR6"},{"doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press (2006)","key":"21_CR7","DOI":"10.7551\/mitpress\/3206.001.0001"},{"unstructured":"Wilson, A.G., Adams, R.P.: Gaussian process kernels for pattern discovery and extrapolation. In: Proceedings of ICML, vol. 28, 1067\u20131075 (2013)","key":"21_CR8"},{"unstructured":"Wilson, A.G., Hu, Z., Salakhutdinov, R., Xing, E.P.: Deep kernel learning. In: Proceedings of AISTATS, vol. 51, pp. 370\u2013378 (2016)","key":"21_CR9"},{"key":"21_CR10","doi-asserted-by":"publisher","DOI":"10.1017\/9781139061773","volume-title":"Transfer Learning","author":"Q Yang","year":"2020","unstructured":"Yang, Q., Zhang, Y., Dai, W., Pan, S.J.: Transfer Learning. Cambridge University Press, Cambridge (2020)"},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., Dong, Y., Wang, Y., Tang, J., Ding, M.: Prone: fast and scalable network representation learning. In: Proceedings of the IJCAI 2019, pp. 4278\u20134284 (2019)","key":"21_CR11","DOI":"10.24963\/ijcai.2019\/594"},{"doi-asserted-by":"crossref","unstructured":"Zhao, K., Yu, J.X., He, Z., Li, R., Zhang, H.: Lightweight and accurate cardinality estimation by neural network gaussian process. In: SIGMOD 2022, pp. 973\u2013987. ACM (2022)","key":"21_CR12","DOI":"10.1145\/3514221.3526156"},{"doi-asserted-by":"crossref","unstructured":"Zhao, K., Yu, J.X., Zhang, H., Li, Q., Rong, Y.: A learned sketch for subgraph counting. In: Proceedings of SIGMOD 2021 (2021)","key":"21_CR13","DOI":"10.1145\/3448016.3457289"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30675-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:09:13Z","timestamp":1710245353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30675-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031306747","9783031306754"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30675-4_21","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":"15 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"652","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":"125","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":"66","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":"19% - 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","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":"7.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}