{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:22:46Z","timestamp":1743009766463,"version":"3.40.3"},"publisher-location":"Cham","reference-count":60,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030937324"},{"type":"electronic","value":"9783030937331"}],"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\/978-3-030-93733-1_2","type":"book-chapter","created":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T06:02:58Z","timestamp":1645164178000},"page":"22-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adversarial Robustness of\u00a0Probabilistic Network Embedding for\u00a0Link Prediction"],"prefix":"10.1007","author":[{"given":"Xi","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jefrey","family":"Lijffijt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tijl","family":"De Bie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Adamic, L.A., Glance, N.: The political blogosphere and the 2004 U.S. election: divided they blog. In: Proceedings of LinkKDD 2005, pp. 36\u201343 (2005)","DOI":"10.1145\/1134271.1134277"},{"key":"2_CR2","unstructured":"Bojchevski, A., G\u00fcnnemann, S.: Adversarial attacks on node embeddings via graph poisoning. In: Proceedings of the 36th ICML, pp. 695\u2013704 (2019)"},{"key":"2_CR3","unstructured":"Bojchevski, A., G\u00fcnnemann, S.: Certifiable robustness to graph perturbations. In: Proceedings of the 33rd NeurIPS, vol. 32 (2019)"},{"issue":"4","key":"2_CR4","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1109\/TCSS.2020.3004059","volume":"7","author":"J Chen","year":"2020","unstructured":"Chen, J., Lin, X., Shi, Z., Liu, Y.: Link prediction adversarial attack via iterative gradient attack. IEEE Trans. Comput. Soc. Syst. 7(4), 1081\u20131094 (2020)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"2_CR5","unstructured":"Chen, L., et al.: A Survey of Adversarial Learning on Graphs. arXiv preprint arXiv:2003.05730 (2020)"},{"issue":"11","key":"2_CR6","doi-asserted-by":"publisher","first-page":"5043","DOI":"10.3390\/app11115043","volume":"11","author":"X Chen","year":"2021","unstructured":"Chen, X., Kang, B., Lijffijt, J., De Bie, T.: ALPINE: active link prediction using network embedding. Appl. Sci. 11(11), 5043 (2021)","journal-title":"Appl. Sci."},{"key":"2_CR7","unstructured":"Dai, H., et al.: Adversarial attack on graph structured data. In: Proceedings of the 35th ICML, pp. 1115\u20131124 (2018)"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Dai, Q., Li, Q., Tang, J., Wang, D.: Adversarial network embedding. In: Proceedings of the 32nd AAAI, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11865"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Dai, Q., Shen, X., Zhang, L., Li, Q., Wang, D.: Adversarial training methods for network embedding. In: Proceedings of the 28th WWW, pp. 329\u2013339 (2019)","DOI":"10.1145\/3308558.3313445"},{"issue":"1","key":"2_CR10","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s11280-013-0240-6","volume":"18","author":"AM Fard","year":"2015","unstructured":"Fard, A.M., Wang, K.: Neighborhood randomization for link privacy in social network analysis. World Wide Web 18(1), 9\u201332 (2015)","journal-title":"World Wide Web"},{"issue":"6","key":"2_CR11","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1109\/TKDE.2019.2957786","volume":"33","author":"F Feng","year":"2021","unstructured":"Feng, F., He, X., Tang, J., Chua, T.-S.: Graph adversarial training: dynamically regularizing based on graph structure. IEEE Trans. Knowl. Data Eng. 33(6), 2493\u20132504 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Gao, Z., Hu, R., Gong, Y.: Certified robustness of graph classification against topology attack with randomized smoothing. In: Proceedings of the GLOBECOM 2020, pp. 1\u20136 (2020)","DOI":"10.1109\/GLOBECOM42002.2020.9322576"},{"key":"2_CR13","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: Proceedings of the 3rd ICLR (2015)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings of 2005 IEEE IJCNN, vol. 2, pp. 729\u2013734 (2005)","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"2_CR16","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st NeurIPS, vol. 30 (2017)"},{"key":"2_CR17","unstructured":"Handcock, M.S., Hunter, D.R., Butts, C.T., Goodreau, S.M., Morris, M.: statnet: An R package for the Statistical Modeling of Social Networks (2003). http:\/\/www.csde.washington.edu\/statnet"},{"key":"2_CR18","doi-asserted-by":"publisher","unstructured":"IEEE: IEEE Standard Glossary of Software Engineering Terminology. IEEE STD 610.12-1990, pp. 1\u201384 (1990). https:\/\/doi.org\/10.1109\/IEEESTD.1990.101064","DOI":"10.1109\/IEEESTD.1990.101064"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Jia, J., Wang, B., Cao, X., Gong, N.Z.: Certified robustness of community detection against adversarial structural perturbation via randomized smoothing. In: Proceedings of the 29th WWW, pp. 2718\u20132724 (2020)","DOI":"10.1145\/3366423.3380029"},{"key":"2_CR20","unstructured":"Jin, H., Shi, Z., Peruri, V.J.S.A., Zhang, X.: Certified robustness of graph convolution networks for graph classification under topological attacks. In: Proceedings of the 34th NeurIPS, vol. 33, pp. 8463\u20138474 (2020)"},{"key":"2_CR21","unstructured":"Jin, W., Li, Y., Xu, H., Wang, Y., Tang, J.: Adversarial attacks and defenses on graphs: a review and empirical study. arXiv preprint arXiv:2003.00653 (2020)"},{"key":"2_CR22","unstructured":"Kang, B., Lijffijt, J., De Bie, T.: Conditional network embeddings. In: Proceedings of the 7th ICLR (2019)"},{"key":"2_CR23","unstructured":"Kang, B., Lijffijt, J., De Bie, T.: ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions. arXiv preprint arXiv:1904.12694 (2019)"},{"key":"2_CR24","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th ICLR (2017)"},{"issue":"7","key":"2_CR25","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1002\/asi.20591","volume":"58","author":"D Liben-Nowell","year":"2007","unstructured":"Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019\u20131031 (2007)","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"key":"2_CR26","doi-asserted-by":"crossref","unstructured":"Lin, W., Ji, S., Li, B.: Adversarial attacks on link prediction algorithms based on graph neural networks. In: Proceedings of the 15th ACM AsiaCCS, pp. 370\u2013380 (2020)","DOI":"10.1145\/3320269.3384750"},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.aiopen.2021.02.001","volume":"2","author":"X Liu","year":"2021","unstructured":"Liu, X., Tang, J.: Network representation learning: a macro and micro view. AI Open 2, 43\u201364 (2021)","journal-title":"AI Open"},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Liu, Z., Larson, M.: Adversarial item promotion: vulnerabilities at the core of top-N recommenders that use images to address cold start. In: Proceedings of the 30th WWW, pp. 3590\u20133602 (2021)","DOI":"10.1145\/3442381.3449891"},{"key":"2_CR29","unstructured":"Ma, Y., Wang, S., Derr, T., Wu, L., Tang, J.: Attacking Graph Convolutional Networks via Rewiring. arXiv preprint arXiv:1906.03750 (2019)"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Mara, A.C., Lijffijt, J., De Bie, T.: Benchmarking network embedding models for link prediction: are we making progress? In: Proceedings of the 7th IEEE DSAA, pp. 138\u2013147 (2020)","DOI":"10.1109\/DSAA49011.2020.00026"},{"issue":"4","key":"2_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3012704","volume":"49","author":"V Mart\u00ednez","year":"2016","unstructured":"Mart\u00ednez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. ACM Comput. Surv. 49(4), 1\u201333 (2016)","journal-title":"ACM Comput. Surv."},{"key":"2_CR32","unstructured":"Mirzasoleiman, B., Cao, K., Leskovec, J.: Coresets for robust training of deep neural networks against noisy labels. In: Proceedings of the 34th NeurIPS, vol. 33, pp. 11465\u201311477 (2020)"},{"issue":"8","key":"2_CR33","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","unstructured":"Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE PAMI 41(8), 1979\u20131993 (2018)","journal-title":"IEEE PAMI"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. In: Proceedings of the 27th IJCAI, pp. 2609\u20132615 (2018)","DOI":"10.24963\/ijcai.2018\/362"},{"key":"2_CR35","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, pp. 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying DeepWalk, LINE, PTE, and node2vec. In: Proceedings of the 11th ACM WSDM, pp. 459\u2013467 (2018)","DOI":"10.1145\/3159652.3159706"},{"key":"2_CR37","unstructured":"Sun, L., et al.: Adversarial attack and defense on graph data: a survey. arXiv preprint arXiv:1812.10528 (2018)"},{"key":"2_CR38","unstructured":"Sun, M., et al.: Data poisoning attack against unsupervised node embedding methods. arXiv preprint arXiv:1810.12881 (2018)"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th WWW, pp. 1067\u20131077 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"2_CR40","doi-asserted-by":"crossref","unstructured":"Tang, X., Li, Y., Sun, Y., Yao, H., Mitra, P., Wang, S.: Transferring robustness for graph neural network against poisoning attacks. In: Proceedings of the 13th WSDM, pp. 600\u2013608 (2020)","DOI":"10.1145\/3336191.3371851"},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Proceedings of the 31st AAAI, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10488"},{"issue":"2","key":"2_CR42","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.P., Wooldridge, M.J., Rahwan, T.: Hiding individuals and communities in a social network. Nat. Hum. Behav. 2(2), 139\u2013147 (2018)","journal-title":"Nat. Hum. Behav."},{"issue":"1","key":"2_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48583-6","volume":"9","author":"M Waniek","year":"2019","unstructured":"Waniek, M., Zhou, K., Vorobeychik, Y., Moro, E., Michalak, T.P., Rahwan, T.: How to hide one\u2019s relationships from link prediction algorithms. Sci. Rep. 9(1), 1\u201310 (2019)","journal-title":"Sci. Rep."},{"issue":"6684","key":"2_CR44","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/30918","volume":"393","author":"DJ Watts","year":"1998","unstructured":"Watts, D.J., Strogatz, S.H.: Collective dynamics of \u2018small-world\u2019 networks. Nature 393(6684), 440\u2013442 (1998)","journal-title":"Nature"},{"key":"2_CR45","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: Proceedings of the 28th IJCAI, pp. 4816\u20134823 (2019)","DOI":"10.24963\/ijcai.2019\/669"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Xu, K., et al.: Topology attack and defense for graph neural networks: an optimization perspective. In: Proceedings of the 28th IJCAI, pp. 3961\u20133967 (2019)","DOI":"10.24963\/ijcai.2019\/550"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Yang, G., Gong, N.Z., Cai, Y.: Fake co-visitation injection attacks to recommender systems. In: Proceedings of the 24th NDSS (2017)","DOI":"10.14722\/ndss.2017.23020"},{"issue":"2","key":"2_CR48","first-page":"754","volume":"33","author":"S Yu","year":"2021","unstructured":"Yu, S., et al.: Target defense against link-prediction-based attacks via evolutionary perturbations. IEEE Trans. Knowl. Data Eng. 33(2), 754\u2013767 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"2_CR49","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1086\/jar.33.4.3629752","volume":"33","author":"WW Zachary","year":"1977","unstructured":"Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452\u2013473 (1977)","journal-title":"J. Anthropol. Res."},{"key":"2_CR50","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, Y., Ding, B., Gao, J.: Practical data poisoning attack against next-item recommendation. In: Proceedings of the 29th WWW, pp. 2458\u20132464 (2020)","DOI":"10.1145\/3366423.3379992"},{"key":"2_CR51","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Proceedings of the 32nd NeurIPS, vol. 31 (2018)"},{"key":"2_CR52","unstructured":"Zheng, C., et al.: Robust graph representation learning via neural sparsification. In: Proceedings of the 37th ICML, pp. 11458\u201311468 (2020)"},{"key":"2_CR53","doi-asserted-by":"crossref","unstructured":"Zhou, K., Michalak, T.P., Vorobeychik, Y.: Adversarial robustness of similarity-based link prediction. In: Proceedings of the 19th IEEE ICDM, pp. 926\u2013935 (2019)","DOI":"10.1109\/ICDM.2019.00103"},{"key":"2_CR54","unstructured":"Zhou, K., Michalak, T.P., Waniek, M., Rahwan, T., Vorobeychik, Y.: Attacking similarity-based link prediction in social networks. In: Proceedings of the 18th AAMAS, pp. 305\u2013313 (2019)"},{"key":"2_CR55","doi-asserted-by":"crossref","unstructured":"Zhu, D., Zhang, Z., Cui, P., Zhu, W.: Robust graph convolutional networks against adversarial attacks. In: Proceedings of the 25th ACM SIGKDD, pp. 1399\u20131407 (2019)","DOI":"10.1145\/3292500.3330851"},{"key":"2_CR56","doi-asserted-by":"crossref","unstructured":"Z\u00fcgner, D., Akbarnejad, A., G\u00fcnnemann, S.: Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD, pp. 2847\u20132856 (2018)","DOI":"10.1145\/3219819.3220078"},{"issue":"5","key":"2_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3394520","volume":"14","author":"D Z\u00fcgner","year":"2020","unstructured":"Z\u00fcgner, D., Borchert, O., Akbarnejad, A., Guennemann, S.: Adversarial attacks on graph neural networks: perturbations and their patterns. ACM Trans. Knowl. Discov. Data 14(5), 1\u201331 (2020)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"2_CR58","doi-asserted-by":"crossref","unstructured":"Z\u00fcgner, D., G\u00fcnnemann, S.: Adversarial attacks on graph neural networks via meta learning. In: Proceedings of the 7th ICLR (2019)","DOI":"10.24963\/ijcai.2019\/872"},{"key":"2_CR59","doi-asserted-by":"crossref","unstructured":"Z\u00fcgner, D., G\u00fcnnemann, S.: Certifiable robustness and robust training for graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD, pp. 246\u2013256 (2019)","DOI":"10.1145\/3292500.3330905"},{"key":"2_CR60","doi-asserted-by":"crossref","unstructured":"Z\u00fcgner, D., G\u00fcnnemann, S.: Certifiable robustness of graph convolutional networks under structure perturbations. In: Proceedings of the 26th ACM SIGKDD, pp. 1656\u20131665 (2020)","DOI":"10.1145\/3394486.3403217"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93733-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T21:21:12Z","timestamp":1726694472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93733-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030937324","9783030937331"],"references-count":60,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93733-1_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"18 February 2022","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":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.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":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}