{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:17:34Z","timestamp":1743041854188,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031226946"},{"type":"electronic","value":"9783031226953"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-22695-3_33","type":"book-chapter","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T15:11:58Z","timestamp":1669993918000},"page":"470-483","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Inspired Fault Detection of\u00a0Dynamical Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-0855","authenticated-orcid":false,"given":"Eugene","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9630-0176","authenticated-orcid":false,"given":"D\u00e9bora C.","family":"Corr\u00eaa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2485-6666","authenticated-orcid":false,"given":"Thomas","family":"Stemler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5378-1582","authenticated-orcid":false,"given":"Michael","family":"Small","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"issue":"17","key":"33_CR1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.93.174102","volume":"93","author":"DM Abrams","year":"2004","unstructured":"Abrams, D.M., Strogatz, S.H.: Chimera states for coupled oscillators. Phys. Rev. Lett. 93(17), 174102 (2004)","journal-title":"Phys. Rev. Lett."},{"issue":"2","key":"33_CR2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.100.022224","volume":"100","author":"A Andreev","year":"2019","unstructured":"Andreev, A., Frolov, N., Pisarchik, A., Hramov, A.: Chimera state in complex networks of bistable Hodgkin-Huxley neurons. Phys. Rev. E 100(2), 022224 (2019)","journal-title":"Phys. Rev. E"},{"issue":"3","key":"33_CR3","volume":"11","author":"A Banerjee","year":"2021","unstructured":"Banerjee, A., Hart, J.D., Roy, R., Ott, E.: Machine learning link inference of noisy delay-coupled networks with optoelectronic experimental tests. Phys. Rev. X 11(3), 031014 (2021)","journal-title":"Phys. Rev. X"},{"key":"33_CR4","doi-asserted-by":"crossref","unstructured":"Banerjee, A., Pathak, J., Roy, R., Restrepo, J.G., Ott, E.: Using machine learning to assess short term causal dependence and infer network links. Chaos Interdisc. J. Nonlinear Sci. 29(12), 121104 (2019)","DOI":"10.1063\/1.5134845"},{"issue":"1","key":"33_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-017-02288-4","volume":"8","author":"J Casadiego","year":"2017","unstructured":"Casadiego, J., Nitzan, M., Hallerberg, S., Timme, M.: Model-free inference of direct network interactions from nonlinear collective dynamics. Nat. Commun. 8(1), 1\u201310 (2017)","journal-title":"Nat. Commun."},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Dashtdar, M., Dashti, R., Shaker, H.R.: Distribution network fault section identification and fault location using artificial neural network. In: 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), pp. 273\u2013278. IEEE (2018)","DOI":"10.1109\/ICEEE2.2018.8391345"},{"issue":"2","key":"33_CR7","volume":"10","author":"D Eroglu","year":"2020","unstructured":"Eroglu, D., Tanzi, M., van Strien, S., Pereira, T.: Revealing dynamics, communities, and criticality from data. Phys. Rev. X 10(2), 021047 (2020)","journal-title":"Phys. Rev. X"},{"issue":"2","key":"33_CR8","doi-asserted-by":"publisher","first-page":"S92","DOI":"10.1137\/20M1376923","volume":"60","author":"AR Hota","year":"2021","unstructured":"Hota, A.R., Sneh, T., Gupta, K.: Impacts of game-theoretic activation on epidemic spread over dynamical networks. SIAM J. Control. Optim. 60(2), S92\u2013S118 (2021)","journal-title":"SIAM J. Control. Optim."},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Izhikevich, E.M.: Dynamical systems in neuroscience. MIT press (2007)","DOI":"10.7551\/mitpress\/2526.001.0001"},{"key":"33_CR10","unstructured":"Jaeger, H.: The \u201cecho state\u201d approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology Gesellschaft f\u00fcr Mathematik und Datenverarbeitung mbH (GMD) Technical Report 148, 13 (2001)"},{"key":"33_CR11","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.chaos.2013.07.003","volume":"54","author":"V Kohar","year":"2013","unstructured":"Kohar, V., Sinha, S.: Emergence of epidemics in rapidly varying networks. Chaos Solitons Fractals 54, 127\u2013134 (2013)","journal-title":"Chaos Solitons Fractals"},{"issue":"7","key":"33_CR12","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1007\/s11141-021-10078-8","volume":"63","author":"M Kornilov","year":"2020","unstructured":"Kornilov, M., Sysoev, I., Astakhova, D., Kulminsky, D., Bezruchko, B., Ponomarenko, V.: Reconstruction of the coupling architecture in the ensembles of radio-engineering oscillators by their signals using the methods of granger causality and partial directed coherence. Radiophys. Quantum Electron. 63(7), 542\u2013556 (2020)","journal-title":"Radiophys. Quantum Electron."},{"issue":"2","key":"33_CR13","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1175\/1520-0469(1963)020<0130:DNF>2.0.CO;2","volume":"20","author":"EN Lorenz","year":"1963","unstructured":"Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130\u2013141 (1963)","journal-title":"J. Atmos. Sci."},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Majdandzic, A., et al.: Multiple tipping points and optimal repairing in interacting networks. Nat. Commun. 7(1), 1\u201310 (2016)","DOI":"10.1038\/ncomms10850"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Masuda, N., Lambiotte, R.: A guide to temporal networks. World Scientific (2016)","DOI":"10.1142\/q0033"},{"key":"33_CR16","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.jnca.2016.10.019","volume":"78","author":"T Muhammed","year":"2017","unstructured":"Muhammed, T., Shaikh, R.A.: An analysis of fault detection strategies in wireless sensor networks. J. Netw. Comput. Appl. 78, 267\u2013287 (2017)","journal-title":"J. Netw. Comput. Appl."},{"issue":"2","key":"33_CR17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.77.026103","volume":"77","author":"D Napoletani","year":"2008","unstructured":"Napoletani, D., Sauer, T.D.: Reconstructing the topology of sparsely connected dynamical networks. Phys. Rev. E 77(2), 026103 (2008)","journal-title":"Phys. Rev. E"},{"issue":"11","key":"33_CR18","doi-asserted-by":"publisher","first-page":"2715","DOI":"10.1109\/TAC.2013.2266831","volume":"58","author":"F Pasqualetti","year":"2013","unstructured":"Pasqualetti, F., D\u00f6rfler, F., Bullo, F.: Attack detection and identification in cyber-physical systems. IEEE Trans. Autom. Contr. 58(11), 2715\u20132729 (2013)","journal-title":"IEEE Trans. Autom. Contr."},{"issue":"2160","key":"33_CR19","doi-asserted-by":"publisher","first-page":"20190045","DOI":"10.1098\/rsta.2019.0045","volume":"377","author":"M Rosenblum","year":"2019","unstructured":"Rosenblum, M., Fr\u00fchwirth, M., Moser, M., Pikovsky, A.: Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia. Phil. Trans. R. Soc. A 377(2160), 20190045 (2019)","journal-title":"Phil. Trans. R. Soc. A"},{"issue":"5","key":"33_CR20","doi-asserted-by":"publisher","first-page":"4877","DOI":"10.1103\/PhysRevE.61.4877","volume":"61","author":"M Sachtjen","year":"2000","unstructured":"Sachtjen, M., Carreras, B., Lynch, V.: Disturbances in a power transmission system. Phys. Rev. E 61(5), 4877 (2000)","journal-title":"Phys. Rev. E"},{"issue":"1","key":"33_CR21","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/13\/1\/013004","volume":"13","author":"SG Shandilya","year":"2011","unstructured":"Shandilya, S.G., Timme, M.: Inferring network topology from complex dynamics. New J. Phys. 13(1), 013004 (2011)","journal-title":"New J. Phys."},{"issue":"6","key":"33_CR22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.66.066701","volume":"66","author":"M Small","year":"2002","unstructured":"Small, M., Tse, C.K.: Minimum description length neural networks for time series prediction. Phys. Rev. E 66(6), 066701 (2002)","journal-title":"Phys. Rev. E"},{"issue":"4","key":"33_CR23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.102.042309","volume":"102","author":"G Stepaniants","year":"2020","unstructured":"Stepaniants, G., Brunton, B.W., Kutz, J.N.: Inferring causal networks of dynamical systems through transient dynamics and perturbation. Phys. Rev. E 102(4), 042309 (2020)","journal-title":"Phys. Rev. E"},{"key":"33_CR24","series-title":"Lecture Notes in Mathematics","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1007\/BFb0091924","volume-title":"Dynamical Systems and Turbulence, Warwick 1980","author":"F Takens","year":"1981","unstructured":"Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366\u2013381. Springer, Heidelberg (1981). https:\/\/doi.org\/10.1007\/BFb0091924"},{"key":"33_CR25","unstructured":"Tan, E., Corr\u00eaa, D., Stemler, T., Small, M.: Backpropagation on dynamical networks. arXiv preprint arXiv:2207.03093 (2022)"},{"issue":"5","key":"33_CR26","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.77.050905","volume":"77","author":"M Valencia","year":"2008","unstructured":"Valencia, M., Martinerie, J., Dupont, S., Chavez, M.: Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach. Phys. Rev. E 77(5), 050905 (2008)","journal-title":"Phys. Rev. E"},{"issue":"26","key":"33_CR27","doi-asserted-by":"publisher","first-page":"6671","DOI":"10.1016\/j.physa.2008.08.037","volume":"387","author":"J Wang","year":"2008","unstructured":"Wang, J., Rong, L., Zhang, L., Zhang, Z.: Attack vulnerability of scale-free networks due to cascading failures. Physica A 387(26), 6671\u20136678 (2008)","journal-title":"Physica A"},{"key":"33_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2016.06.004","volume":"644","author":"WX Wang","year":"2016","unstructured":"Wang, W.X., Lai, Y.C., Grebogi, C.: Data based identification and prediction of nonlinear and complex dynamical systems. Phys. Rep. 644, 1\u201376 (2016)","journal-title":"Phys. Rep."},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Weistuch, C., Agozzino, L., Mujica-Parodi, L.R., Dill, K.A.: Inferring a network from dynamical signals at its nodes. PLoS Comput. Biol. 16(11), e1008435 (2020)","DOI":"10.1371\/journal.pcbi.1008435"},{"issue":"6","key":"33_CR30","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0011187","volume":"5","author":"B Wu","year":"2010","unstructured":"Wu, B., Zhou, D., Fu, F., Luo, Q., Wang, L., Traulsen, A.: Evolution of cooperation on stochastic dynamical networks. PLoS ONE 5(6), e11187 (2010)","journal-title":"PLoS ONE"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Wu, H.S.: A survey of research on anomaly detection for time series. In: 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 426\u2013431. IEEE (2016)","DOI":"10.1109\/ICCWAMTIP.2016.8079887"},{"issue":"6","key":"33_CR32","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1016\/j.physa.2009.11.037","volume":"389","author":"Y Xia","year":"2010","unstructured":"Xia, Y., Fan, J., Hill, D.: Cascading failure in Watts-Strogatz small-world networks. Physica A 389(6), 1281\u20131285 (2010)","journal-title":"Physica A"},{"issue":"1","key":"33_CR33","first-page":"65","volume":"55","author":"Y Xia","year":"2008","unstructured":"Xia, Y., Hill, D.J.: Attack vulnerability of complex communication networks. IEEE Trans. Circuits Syst. II Express Briefs 55(1), 65\u201369 (2008)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"33_CR34","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.physa.2017.02.055","volume":"479","author":"LX Yang","year":"2017","unstructured":"Yang, L.X., Jiang, J.: Impacts of link addition and removal on synchronization of an elementary power network. Physica A 479, 99\u2013107 (2017)","journal-title":"Physica A"},{"key":"33_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2019.106031","volume":"137","author":"J Zenisek","year":"2019","unstructured":"Zenisek, J., Holzinger, F., Affenzeller, M.: Machine learning based concept drift detection for predictive maintenance. Comput. Ind. Eng. 137, 106031 (2019)","journal-title":"Comput. Ind. Eng."},{"issue":"2","key":"33_CR36","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1088\/0256-307X\/17\/2\/004","volume":"17","author":"JS Zhang","year":"2000","unstructured":"Zhang, J.S., Xiao, X.C.: Predicting chaotic time series using recurrent neural network. Chin. Phys. Lett. 17(2), 88 (2000)","journal-title":"Chin. Phys. Lett."},{"issue":"1","key":"33_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-019-0069-y","volume":"6","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Tong, H., Xu, J., Maciejewski, R.: Graph convolutional networks: a comprehensive review. Comput. Soc. Netw. 6(1), 1\u201323 (2019). https:\/\/doi.org\/10.1186\/s40649-019-0069-y","journal-title":"Comput. Soc. Netw."},{"issue":"2","key":"33_CR38","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.89.022914","volume":"89","author":"Y Zhu","year":"2014","unstructured":"Zhu, Y., Zheng, Z., Yang, J.: Chimera states on complex networks. Phys. Rev. E 89(2), 022914 (2014)","journal-title":"Phys. Rev. E"}],"container-title":["Lecture Notes in Computer Science","AI 2022: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22695-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:28:44Z","timestamp":1710257324000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22695-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031226946","9783031226953"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22695-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"3 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australasian Joint Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"35","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ausai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ajcai2022.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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"90","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":"56","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":"62% - 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":"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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}