{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:37:14Z","timestamp":1742931434096,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319461274"},{"type":"electronic","value":"9783319461281"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46128-1_4","type":"book-chapter","created":{"date-parts":[[2016,9,3]],"date-time":"2016-09-03T05:34:23Z","timestamp":1472880863000},"page":"49-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Incremental Commute Time Using Random Walks and Online Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Nguyen Lu Dang","family":"Khoa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjay","family":"Chawla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,9,4]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1016\/j.patcog.2007.10.002","volume":"41","author":"RK Agrawal","year":"2008","unstructured":"Agrawal, R.K.: Karmeshu: Perturbation scheme for online learning of features: Incremental principal component analysis. Pattern Recogn. 41, 1452\u20131460 (2008)","journal-title":"Pattern Recogn."},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Bay, S.D., Schwabacher, M.: Mining distance-based outliers in near linear timewith randomization and a simple pruning rule. In: KDD 2003: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 29\u201338. ACM, New York, NY, USA (2003)","DOI":"10.1145\/956750.956758"},{"issue":"3","key":"4_CR3","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TKDE.2007.46","volume":"19","author":"F Fouss","year":"2007","unstructured":"Fouss, F., Renders, J.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":"4_CR4","unstructured":"Frank, A., Asuncion, A.: Uci machine learning repository (2010)"},{"issue":"2","key":"4_CR5","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1137\/1015032","volume":"15","author":"GH Golub","year":"1973","unstructured":"Golub, G.H.: Some modified matrix eigenvalue problems. SIAM Rev. 15(2), 318\u2013334 (1973)","journal-title":"SIAM Rev."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1137\/S089547989223924X","volume":"15","author":"M Gu","year":"1994","unstructured":"Gu, M., Eisenstat, S.C.: A stable and efficient algorithm for the rank-one modification of the symmetric eigenproblem. SIAM J. Matrix Anal. Appl. 15, 1266\u20131276 (1994)","journal-title":"SIAM J. Matrix Anal. Appl."},{"key":"4_CR7","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1007\/978-3-642-13672-6_41","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Nguyen Lu Dang Khoa","year":"2010","unstructured":"Khoa, N.L.D., Chawla, S.: Robust outlier detection using commute time andeigenspace embedding. In: PAKDD 2010: Proceedings of the The 14th Pacific-AsiaConference on Knowledge Discovery and Data Mining, pp. 422\u2013434. Springer, Berlin\/Heidelberg (2010)"},{"issue":"4","key":"4_CR8","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1177\/1475921714532989","volume":"13","author":"NLD Khoa","year":"2014","unstructured":"Khoa, N.L.D., Zhang, B., Wang, Y., Chen, F., Mustapha, S.: Robust dimensionality reduction and damage detection approaches in structural health monitoring. Struct. Health Monit. 13(4), 406\u2013417 (2014)","journal-title":"Struct. Health Monit."},{"key":"4_CR9","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1007\/978-3-642-10331-5_99","volume-title":"Advances in Visual Computing","author":"Ioannis Koutis","year":"2009","unstructured":"Koutis, I., Miller, G.L., Tolliver, D.: Combinatorial preconditioners andmultilevel solvers for problems in computer vision and image processing. In: Proceedings of the 5th International Symposium on Advances in VisualComputing: Part I, pp. 1067\u20131078. ISVC 2009, Springer-Verlag, Berlin, Heidelberg (2009)"},{"key":"4_CR10","first-page":"1","volume":"2","author":"L Lov\u00e1sz","year":"1993","unstructured":"Lov\u00e1sz, L.: Random walks on graphs: a survey. Comb. Paul Erd\u00f6s is Eighty 2, 1\u201346 (1993)","journal-title":"Comb. Paul Erd\u00f6s is Eighty"},{"issue":"4","key":"4_CR11","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Ning, H., Xu, W., Chi, Y., Gong, Y., Huang, T.: Incremental spectral clustering with application to monitoring of evolving blog communities. In: SIAM International Conference on Data Mining (2007)","DOI":"10.1137\/1.9781611972771.24"},{"key":"4_CR13","unstructured":"Purnamrita Sarkar, A.W.M.: A tractable approach to finding closest truncated-commute-time neighbors in large graphs. In: The 23rd Conference on Uncertainty in Artificial Intelligence (UAI) (2007)"},{"issue":"11","key":"4_CR14","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1109\/TPAMI.2007.1103","volume":"29","author":"H Qiu","year":"2007","unstructured":"Qiu, H., Hancock, E.: Clustering and embedding using commute times. IEEE TPAMI 29(11), 1873\u20131890 (2007)","journal-title":"IEEE TPAMI"},{"key":"4_CR15","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/978-3-540-30115-8_35","volume-title":"Machine Learning: ECML 2004","author":"Marco Saerens","year":"2004","unstructured":"Saerens, M., Fouss, F., Yen, L., Dupont, P.: The principal components analysisof a graph, and its relationships to spectral clustering. In: Proceedings of the15th European Conference on Machine Learning (ECML 2004), pp. 371\u2013383. Springer-Verlag, Heidelberg (2004)"},{"key":"4_CR16","doi-asserted-by":"crossref","unstructured":"Sarkar, P., Moore, A.W., Prakash, A.: Fast incremental proximity search in large graphs. In: Proceedings of the 25th International Conference on Machine Learning, pp. 896\u2013903. ICML 2008, NY, USA. ACM, New York (2008)","DOI":"10.1145\/1390156.1390269"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Spielman, D.A., Srivastava, N.: Graph sparsification by effective resistances. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, pp. 563\u2013568. STOC 2008, NY, USA. ACM, New York (2008)","DOI":"10.1145\/1374376.1374456"},{"key":"4_CR18","unstructured":"Spielman, D.A., Teng, S.H.: Nearly-linear time algorithms for preconditioning and solving symmetric, diagonally dominant linear systems. CoRR abs\/cs\/0607105 (2006)"},{"key":"4_CR19","doi-asserted-by":"crossref","unstructured":"Venkatasubramanian, S., Wang, Q.: The johnson-lindenstrauss transform: An empirical study. In: Mller-Hannemann, M., Werneck, R.F.F. (ed.) ALENEX, pp. 164\u2013173. SIAM (2011)","DOI":"10.1137\/1.9781611972917.16"},{"key":"4_CR20","doi-asserted-by":"publisher","first-page":"1675","DOI":"10.1007\/s11276-009-0221-y","volume":"16","author":"ZR Zaidi","year":"2009","unstructured":"Zaidi, Z.R., Hakami, S., Landfeldt, B., Moors, T.: Real-time detection of traffic anomalies in wireless mesh networks. Wirel. Netw. 16, 1675\u20131689 (2009)","journal-title":"Wirel. Netw."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46128-1_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:09:51Z","timestamp":1630800591000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-46128-1_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319461274","9783319461281"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46128-1_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"4 September 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Riva del Garda","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":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}