{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:09:06Z","timestamp":1773817746116,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2009,6,14]],"date-time":"2009-06-14T00:00:00Z","timestamp":1244937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000143","name":"Division of Computing and Communication Foundations","doi-asserted-by":"publisher","award":["CCF-0634957CCF-0843915"],"award-info":[{"award-number":["CCF-0634957CCF-0843915"]}],"id":[{"id":"10.13039\/100000143","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2009,6,14]]},"DOI":"10.1145\/1553374.1553400","type":"proceedings-article","created":{"date-parts":[[2009,6,16]],"date-time":"2009-06-16T13:34:36Z","timestamp":1245159276000},"page":"201-208","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":62,"title":["Fitting a graph to vector data"],"prefix":"10.1145","author":[{"given":"Samuel I.","family":"Daitch","sequence":"first","affiliation":[{"name":"Yale University, New Haven, CT"}]},{"given":"Jonathan A.","family":"Kelner","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA"}]},{"given":"Daniel A.","family":"Spielman","sequence":"additional","affiliation":[{"name":"Yale University, New Haven, CT"}]}],"member":"320","published-online":{"date-parts":[[2009,6,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proc. Sys. 18 (pp. 67--74)","author":"Argyriou A.","year":"2005","unstructured":"Argyriou , A. , Herbster , M. , & Pontil , M. ( 2005 ). Combining graph laplacians for semi-supervised learning. Adv. in Neural Inf . Proc. Sys. 18 (pp. 67--74) . Argyriou, A., Herbster, M., & Pontil, M. (2005). Combining graph laplacians for semi-supervised learning. Adv. in Neural Inf. Proc. Sys. 18 (pp. 67--74)."},{"key":"e_1_3_2_1_2_1","volume-title":"UCI machine learning repository","author":"Asuncion A.","year":"2007","unstructured":"Asuncion , A. , & Newman , D. ( 2007 ). UCI machine learning repository . http:\/\/www.ics.uci.edu\/?mlearn\/MLRepository.htm Asuncion, A., & Newman, D. (2007). UCI machine learning repository. http:\/\/www.ics.uci.edu\/?mlearn\/MLRepository.htm"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2004.1326716"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1162\/089976603321780317"},{"key":"e_1_3_2_1_5_1","volume-title":"LIBSVM: a library for support vector machines. Software","author":"Chang C.-C.","year":"2001","unstructured":"Chang , C.-C. , & Lin , C.-J. ( 2001 ). LIBSVM: a library for support vector machines. Software available at http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvm."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2005.07.005"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0500334102"},{"key":"e_1_3_2_1_8_1","volume-title":"Proc. 20th Int. Conf. on Mach. Learn. (pp. 290--297)","author":"Joachims T.","year":"2003","unstructured":"Joachims , T. ( 2003 ). Transductive learning via spectral graph partitioning . Proc. 20th Int. Conf. on Mach. Learn. (pp. 290--297) . Joachims, T. (2003). Transductive learning via spectral graph partitioning. Proc. 20th Int. Conf. on Mach. Learn. (pp. 290--297)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-007-9052-3"},{"key":"e_1_3_2_1_10_1","volume-title":"Proc. Sys. 21 (pp. 1025--1032)","author":"Maier M.","year":"2008","unstructured":"Maier , M. , von Luxburg , U. , & Hein , M. ( 2008 ). Influence of graph construction on graph-based clustering measures. Adv. in Neural Inf . Proc. Sys. 21 (pp. 1025--1032) . Maier, M., von Luxburg, U., & Hein, M. (2008). Influence of graph construction on graph-based clustering measures. Adv. in Neural Inf. Proc. Sys. 21 (pp. 1025--1032)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/874063.875554"},{"key":"e_1_3_2_1_12_1","volume-title":"Proc. Sys. 14 (pp. 849--856)","author":"Ng A. Y.","year":"2001","unstructured":"Ng , A. Y. , Jordan , M. I. , & Weiss , Y. ( 2001 ). On spectral clustering: Analysis and an algorithm. Adv. in Neural Inf . Proc. Sys. 14 (pp. 849--856) . Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. Adv. in Neural Inf. Proc. Sys. 14 (pp. 849--856)."},{"key":"e_1_3_2_1_13_1","volume-title":"C4.5: Programs for machine learning","author":"Quinlan J. R.","year":"1993","unstructured":"Quinlan , J. R. ( 1993 ). C4.5: Programs for machine learning . San Francisco : Morgan Kaufmann . Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102455"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007352.1007372"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143957"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1080\/10556789908805762"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-002-0347-5"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390300"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-005-4258-6"},{"key":"e_1_3_2_1_22_1","volume-title":"The spider","author":"Weston J.","year":"2008","unstructured":"Weston , J. , Elisseeff , A. , BakIr , G. , & Sinz , F. ( 2008 ). The spider . http:\/\/www.kyb.mpg.de\/bs\/people\/spider\/. Weston, J., Elisseeff, A., BakIr, G., & Sinz, F. (2008). The spider. http:\/\/www.kyb.mpg.de\/bs\/people\/spider\/."},{"key":"e_1_3_2_1_24_1","volume-title":"Proc. Sys. 16 (pp. 321--328)","author":"Zhou D.","year":"2003","unstructured":"Zhou , D. , Bousquet , O. , Lal , T. N. , Weston , J. , & Sch ö\u00f6lkopf, B. ( 2003 ). Learning with local and global consistency. Adv. in Neural Inf . Proc. Sys. 16 (pp. 321--328) . Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Schö\u00f6lkopf, B. (2003). Learning with local and global consistency. Adv. in Neural Inf. Proc. Sys. 16 (pp. 321--328)."},{"key":"e_1_3_2_1_25_1","volume-title":"26th DAGM Symposium (pp. 237--244)","author":"Zhou D.","year":"2004","unstructured":"Zhou , D. , & Sch ö\u00f6lkopf, B. ( 2004 a). Learning from labeled and unlabeled data using random walks. Pattern Recognition , 26th DAGM Symposium (pp. 237--244) . Zhou, D., & Schö\u00f6lkopf, B. (2004a). Learning from labeled and unlabeled data using random walks. Pattern Recognition, 26th DAGM Symposium (pp. 237--244)."},{"key":"e_1_3_2_1_26_1","volume-title":"ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields (pp. 132--137)","author":"Zhou D.","year":"2004","unstructured":"Zhou , D. , & Sch ö\u00f6lkopf, B. ( 2004 b). A regularization framework for learning from graph data . ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields (pp. 132--137) . Zhou, D., & Schö\u00f6lkopf, B. (2004b). A regularization framework for learning from graph data. ICML Workshop on Statistical Relational Learning and Its Connections to Other Fields (pp. 132--137)."},{"key":"e_1_3_2_1_27_1","volume-title":"Proc. 20th Int. Conf. on Mach. Learn..","author":"Zhu X.","year":"2003","unstructured":"Zhu , X. , Ghahramani , Z. , & Lafferty , J. D. ( 2003 ). Semi-supervised learning using gaussian fields and harmonic functions . Proc. 20th Int. Conf. on Mach. Learn.. Zhu, X., Ghahramani, Z., & Lafferty, J. D. (2003). Semi-supervised learning using gaussian fields and harmonic functions. Proc. 20th Int. Conf. on Mach. Learn.."}],"event":{"name":"ICML '09: The 26th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming","location":"Montreal Quebec Canada","acronym":"ICML '09","sponsor":["NSF","Microsoft Research Microsoft Research","MITACS"]},"container-title":["Proceedings of the 26th Annual International Conference on Machine Learning"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1553374.1553400","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1553374.1553400","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T13:29:34Z","timestamp":1750253374000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1553374.1553400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,6,14]]},"references-count":26,"alternative-id":["10.1145\/1553374.1553400","10.1145\/1553374"],"URL":"https:\/\/doi.org\/10.1145\/1553374.1553400","relation":{},"subject":[],"published":{"date-parts":[[2009,6,14]]},"assertion":[{"value":"2009-06-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}