{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:47:04Z","timestamp":1742953624167,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030861292"},{"type":"electronic","value":"9783030861308"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86130-8_8","type":"book-chapter","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T12:12:16Z","timestamp":1631103136000},"page":"96-106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Greedy-Based Black-Box Adversarial Attack Scheme on Graph Structure"],"prefix":"10.1007","author":[{"given":"Shushu","family":"Shao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangguo","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: 33rd AAAI Conference on Artificial Intelligence, Honolulu, pp. 7370\u20137377. AAAI (2019)","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"8_CR2","doi-asserted-by":"crossref","unstructured":"Li, J., Rong, Y., Cheng, H., Meng, H., Huang, W., Huang, J.: Semi-supervised graph classification: a hierarchical graph perspective. In: Proceedings of the World Wide Web, San Francisco, pp. 972\u2013982. ACM (2019)","DOI":"10.1145\/3308558.3313461"},{"issue":"9","key":"8_CR3","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai, H., Zheng, V., Chang, K.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616\u20131637 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Z\u00fcgner, D., Akbarnejad, A., G\u00fcnnemann, S.: Adversarial attacks on neural networks for graph data. In: 24th International Conference on Knowledge Discovery and Data Mining, London, pp. 2847\u20132856. ACM (2018)","DOI":"10.1145\/3219819.3220078"},{"issue":"4","key":"8_CR5","first-page":"1","volume":"37","author":"Z Cai","year":"2020","unstructured":"Cai, Z., Xiong, Z., Xu, H., Wang, P., Li, W., Pan, Y.: Generative adversarial networks: a survey towards private and secure applications. ACM Comput. Surv. 37(4), 1\u201337 (2020)","journal-title":"ACM Comput. Surv."},{"issue":"4","key":"8_CR6","first-page":"577","volume":"15","author":"Z Cai","year":"2018","unstructured":"Cai, Z., He, Z., Guan, X., Li, Y.: Collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans. Dependable Secure Comput. 15(4), 577\u2013590 (2018)","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"5","key":"8_CR7","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1109\/JSAC.2020.2980802","volume":"38","author":"X Zheng","year":"2020","unstructured":"Zheng, X., Cai, Z.: Privacy-preserved data sharing towards multiple parties in industrial IoTs. IEEE J. Sel. Areas Commun. 38(5), 968\u2013979 (2020)","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Cai, Z., He, Z.: Trading private range counting over big IoT data. In: 39th IEEE International Conference on Distributed Computing Systems, Dallas, pp. 144\u2013153. IEEE (2019)","DOI":"10.1109\/ICDCS.2019.00023"},{"issue":"2","key":"8_CR9","doi-asserted-by":"publisher","first-page":"766","DOI":"10.1109\/TNSE.2018.2830307","volume":"7","author":"Z Cai","year":"2020","unstructured":"Cai, Z., Zheng, X.: A private and efficient mechanism for data uploading in smart cyber-physical systems. IEEE Trans. Netw. Sci. Eng. 7(2), 766\u2013775 (2020)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Wu, H., Wang, C., Tyshetskiy, Y., Docherty, A., Lu, K., Zhu, L.: Adversarial examples for graph data: deep insights into attack and defense. In: 28th International Joint Conference on Artificial Intelligence, Macao, pp. 4816\u20134823. IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/669"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Chang, H., et al.: A restricted black-box adversarial framework towards attacking graph embedding models. In: 34th AAAI Conference on Artificial Intelligence, New York, pp. 3389\u20133396. AAAI (2020)","DOI":"10.1609\/aaai.v34i04.5741"},{"key":"8_CR12","unstructured":"Dai, H., et al.: Adversarial attack on graph structured data. In: 35th International Conference on Machine Learning, Stockholm, pp. 1115\u20131124. IMLS (2018)"},{"key":"8_CR13","unstructured":"Ma, Y., Wang, S., Wu, L., Tang, J.: Attacking graph convolutional networks via rewiring. arXiv preprint arXiv:1906.03750 (2019)"},{"key":"8_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR, Toulon (2017)"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: 20th International Conference on Knowledge Discovery and Data Mining, New York, pp. 701\u2013710. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"issue":"2","key":"8_CR16","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1038\/s41562-017-0290-3","volume":"2","author":"M Waniek","year":"2018","unstructured":"Waniek, M., Michalak, T., Wooldridge, M., Rahwan, T.: Hiding individuals and communities in a social network. Nat. Hum. Behav. 2(2), 139\u2013147 (2018)","journal-title":"Nat. Hum. Behav."}],"container-title":["Lecture Notes in Computer Science","Wireless Algorithms, Systems, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86130-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T12:28:25Z","timestamp":1631104105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86130-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030861292","9783030861308"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86130-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WASA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless Algorithms, Systems, and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 June 2021","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":"wasa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/wasa-conference.org\/WASA2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","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":"315","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":"103","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":"57","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":"33% - 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":"6","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)"}}]}}