{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T12:20:44Z","timestamp":1726230044782},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442124"},{"type":"electronic","value":"9783031442131"}],"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"},{"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\/978-3-031-44213-1_1","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T05:01:41Z","timestamp":1695272501000},"page":"1-12","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anomaly Detection in\u00a0Directed Dynamic Graphs via\u00a0RDGCN and\u00a0LSTAN"],"prefix":"10.1007","author":[{"given":"Mark Junjie","family":"Li","sequence":"first","affiliation":[]},{"given":"Zukang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xianyu","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Meiting","family":"Li","sequence":"additional","affiliation":[]},{"given":"Gen","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","first-page":"724","DOI":"10.1016\/j.neucom.2022.05.109","volume":"500","author":"M Bansal","year":"2022","unstructured":"Bansal, M., Sharma, D.: Density-based structural embedding for anomaly detection in dynamic networks. Neurocomputing 500, 724\u2013740 (2022)","journal-title":"Neurocomputing"},{"doi-asserted-by":"crossref","unstructured":"Bhatia, S., Hooi, B., Yoon, M., Shin, K., Faloutsos, C.: MIDAS: microcluster-based detector of anomalies in edge streams. In: AAAI, vol. 34, no. 04, pp. 3242\u20133249 (2020)","key":"1_CR2","DOI":"10.1609\/aaai.v34i04.5724"},{"doi-asserted-by":"crossref","unstructured":"Cai, L., et al.: Structural temporal graph neural networks for anomaly detection in dynamic graphs. In: CIKM, pp. 3747\u20133756 (2021)","key":"1_CR3","DOI":"10.1145\/3459637.3481955"},{"doi-asserted-by":"crossref","unstructured":"De Choudhury, M., Sundaram, H., John, A., Seligmann, D.D.: Social synchrony: predicting mimicry of user actions in online social media. In: CSE, vol. 4, pp. 151\u2013158 (2009)","key":"1_CR4","DOI":"10.1109\/CSE.2009.439"},{"unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs\/1810.04805 (2018). http:\/\/arxiv.org\/abs\/1810.04805","key":"1_CR5"},{"key":"1_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/978-3-030-46150-8_24","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"Megha Khosla","year":"2020","unstructured":"Khosla, Megha, Leonhardt, Jurek, Nejdl, Wolfgang, Anand, Avishek: Node representation learning for directed graphs. In: Brefeld, Ulf, Fromont, Elisa, Hotho, Andreas, Knobbe, Arno, Maathuis, Marloes, Robardet, C\u00e9line. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11906, pp. 395\u2013411. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46150-8_24"},{"doi-asserted-by":"crossref","unstructured":"Kumar, S., Hamilton, W.L., Leskovec, J., Jurafsky, D.: Community interaction and conflict on the web. In: WWW, pp. 933\u2013943. International World Wide Web Conferences Steering Committee (2018)","key":"1_CR7","DOI":"10.1145\/3178876.3186141"},{"doi-asserted-by":"crossref","unstructured":"Kumar, S., Hooi, B., Makhija, D., Kumar, M., Faloutsos, C., Subrahmanian, V.: REV2: fraudulent user prediction in rating platforms. In: WSDM, pp. 333\u2013341 (2018)","key":"1_CR8","DOI":"10.1145\/3159652.3159729"},{"doi-asserted-by":"crossref","unstructured":"Kumar, S., Spezzano, F., Subrahmanian, V., Faloutsos, C.: Edge weight prediction in weighted signed networks. In: ICDM, pp. 221\u2013230 (2016)","key":"1_CR9","DOI":"10.1109\/ICDM.2016.0033"},{"doi-asserted-by":"crossref","unstructured":"Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361\u20131370 (2010)","key":"1_CR10","DOI":"10.1145\/1753326.1753532"},{"doi-asserted-by":"crossref","unstructured":"Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177\u2013187 (2005)","key":"1_CR11","DOI":"10.1145\/1081870.1081893"},{"unstructured":"Lippmann, R., et al.: Results of the DARPA 1998 offline intrusion detection evaluation. In: Recent Advances in Intrusion Detection, vol. 99, pp. 829\u2013835 (1999)","key":"1_CR12"},{"doi-asserted-by":"crossref","unstructured":"Ma, X., et al.: A comprehensive survey on graph anomaly detection with deep learning. TKDE abs\/2106.07178, 1 (2021)","key":"1_CR13","DOI":"10.1109\/TKDE.2021.3118815"},{"issue":"2","key":"1_CR14","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.socnet.2009.02.002","volume":"31","author":"T Opsahl","year":"2009","unstructured":"Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31(2), 155\u2013163 (2009)","journal-title":"Soc. Netw."},{"doi-asserted-by":"crossref","unstructured":"Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: SIGKDD, pp. 1105\u20131114 (2016)","key":"1_CR15","DOI":"10.1145\/2939672.2939751"},{"doi-asserted-by":"crossref","unstructured":"Ranshous, S., Harenberg, S., Sharma, K., Samatova, N.F.: A scalable approach for outlier detection in edge streams using sketch-based approximations. In: SIAM, pp. 189\u2013197 (2016)","key":"1_CR16","DOI":"10.1137\/1.9781611974348.22"},{"issue":"3","key":"1_CR17","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/wics.1347","volume":"7","author":"S Ranshous","year":"2015","unstructured":"Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdiscip. Rev. Comput. Stat. 7(3), 223\u2013247 (2015)","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"5","key":"1_CR18","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TCYB.2019.2906078","volume":"51","author":"L Wang","year":"2021","unstructured":"Wang, L., Yu, Z., Xiong, F., Yang, D., Pan, S., Yan, Z.: Influence spread in geo-social networks: a multiobjective optimization perspective. IEEE Trans. Cybern. 51(5), 2663\u20132675 (2021)","journal-title":"IEEE Trans. Cybern."},{"doi-asserted-by":"crossref","unstructured":"Yu, W., Cheng, W., Aggarwal, C.C., Zhang, K., Chen, H., Wang, W.: NetWalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: SIGKDD, pp. 2672\u20132681 (2018)","key":"1_CR19","DOI":"10.1145\/3219819.3220024"},{"doi-asserted-by":"crossref","unstructured":"Zheng, L., Li, Z., Li, J., Li, Z., Gao, J.: AddGraph: anomaly detection in dynamic graph using attention-based temporal GCN. In: IJCAI, pp. 4419\u20134425 (2019)","key":"1_CR20","DOI":"10.24963\/ijcai.2019\/614"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44213-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:17:46Z","timestamp":1695277066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44213-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442124","9783031442131"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44213-1_1","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":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}