{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:31:27Z","timestamp":1742913087342,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030602475"},{"type":"electronic","value":"9783030602482"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60248-2_4","type":"book-chapter","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T09:03:14Z","timestamp":1601370194000},"page":"50-64","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Content-Aware Anomaly Detection with\u00a0Network Representation Learning"],"prefix":"10.1007","author":[{"given":"Zhong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanzhi","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Yu, W., et al.: Netwalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)","DOI":"10.1145\/3219819.3220024"},{"key":"4_CR2","doi-asserted-by":"crossref","unstructured":"Hu, R., et al.: An embedding approach to anomaly detection. In: IEEE 32nd International Conference on Data Engineering (ICDE). IEEE (2016)","DOI":"10.1109\/ICDE.2016.7498256"},{"key":"4_CR3","doi-asserted-by":"crossref","unstructured":"Gao, J., et al.: A spectral framework for detecting inconsistency across multi-source object relationships. In: 2011 IEEE 11th International Conference on Data Mining. IEEE (2011)","DOI":"10.1109\/ICDM.2011.16"},{"issue":"4","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1109\/SURV.2013.013013.00155","volume":"15","author":"A Fischer","year":"2013","unstructured":"Fischer, A., et al.: Virtual network embedding: a survey. IEEE Commun. Surv. Tutorials 15(4), 1888\u20131906 (2013)","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"4_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-642-13672-6_40","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"L Akoglu","year":"2010","unstructured":"Akoglu, L., McGlohon, M., Faloutsos, C.: Oddball: spotting anomalies in weighted graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6119, pp. 410\u2013421. Springer, Heidelberg (2010). \nhttps:\/\/doi.org\/10.1007\/978-3-642-13672-6_40"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"4_CR9","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"4_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/978-3-642-13672-6_42","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"BA Prakash","year":"2010","unstructured":"Prakash, B.A., Sridharan, A., Seshadri, M., Machiraju, S., Faloutsos, C.: EigenSpokes: surprising patterns and scalable community chipping in large graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS (LNAI), vol. 6119, pp. 435\u2013448. Springer, Heidelberg (2010). \nhttps:\/\/doi.org\/10.1007\/978-3-642-13672-6_42"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, M., et al.: A general suspiciousness metric for dense blocks in multimodal data. In: IEEE International Conference on Data Mining. IEEE (2015)","DOI":"10.1109\/ICDM.2015.61"},{"key":"4_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-319-46128-1_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Shin","year":"2016","unstructured":"Shin, K., Hooi, B., Faloutsos, C.: M-Zoom: fast dense-block detection in tensors with quality guarantees. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 264\u2013280. Springer, Cham (2016). \nhttps:\/\/doi.org\/10.1007\/978-3-319-46128-1_17"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Shin, K., et al.: D-cube: dense-block detection in terabyte-scale tensors. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM (2017)","DOI":"10.1145\/3018661.3018676"},{"key":"4_CR14","doi-asserted-by":"crossref","unstructured":"Tong, H., Lin, C.-Y.: Non-negative residual matrix factorization with application to graph anomaly detection. In: Proceedings of the 2011 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2011)","DOI":"10.1137\/1.9781611972818.13"},{"key":"4_CR15","doi-asserted-by":"crossref","unstructured":"Tu, C., et al.: Cane: context-aware network embedding for relation modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2017)","DOI":"10.18653\/v1\/P17-1158"},{"issue":"1","key":"4_CR16","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s13278-018-0503-4","volume":"8","author":"D Kagan","year":"2018","unstructured":"Kagan, D., Elovichi, Y., Fire, M.: Generic anomalous vertices detection utilizing a link prediction algorithm. Social Netw. Anal. Min. 8(1), 27 (2018)","journal-title":"Social Netw. Anal. Min."},{"key":"4_CR17","unstructured":"Kagan, D., Fire, M., Elovici, Y.: Unsupervised anomalous vertices detection utilizing link prediction algorithms. arXiv preprint \narXiv:1610.07525\n\n (2016)"},{"key":"4_CR18","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: Eighth IEEE International Conference on Data Mining. IEEE (2008)","DOI":"10.1109\/ICDM.2008.17"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60248-2_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T10:18:12Z","timestamp":1601374692000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60248-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030602475","9783030602482"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60248-2_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New York, NY","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cloud-conf.net\/ica3pp2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"495","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":"142","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":"5","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":"29% - 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":"305","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":"10","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)"}}]}}