{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T19:31:31Z","timestamp":1749670291035,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863647"},{"type":"electronic","value":"9783030863654"}],"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\/978-3-030-86365-4_17","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:02:39Z","timestamp":1631271759000},"page":"204-216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Parameterized Hypercomplex Graph Neural Networks for Graph Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7634-502X","authenticated-orcid":false,"given":"Tuan","family":"Le","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3502-7521","authenticated-orcid":false,"given":"Marco","family":"Bertolini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4169-9324","authenticated-orcid":false,"given":"Frank","family":"No\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4191-2156","authenticated-orcid":false,"given":"Djork-Arn\u00e9","family":"Clevert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"17_CR1","unstructured":"Arjovsky, M., Shah, A., Bengio, Y.: Unitary evolution recurrent neural networks. In: International Conference on Machine Learning, pp. 1120\u20131128 (2016)"},{"key":"17_CR2","unstructured":"Beaini, D., Passaro, S., L\u00e9tourneau, V., Hamilton, W.L., Corso, G., Li\u00f2, P.: Directional graph networks (2020)"},{"issue":"4","key":"17_CR3","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18\u201342 (2017)","journal-title":"IEEE Signal Process. Mag."},{"key":"17_CR4","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs (2014)"},{"key":"17_CR5","unstructured":"Chami, I., Abu-El-Haija, S., Perozzi, B., R\u00e9, C., Murphy, K.: Machine learning on graphs: a model and comprehensive taxonomy (2021)"},{"key":"17_CR6","unstructured":"Chami, I., Ying, Z., R\u00e9, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 4868\u20134879. Curran Associates, Inc. (2019)"},{"key":"17_CR7","unstructured":"Corso, G., Cavalleri, L., Beaini, D., Li\u00f2, P., Veli\u010dkovi\u0107, P.: Principal neighbourhood aggregation for graph nets (2020)"},{"key":"17_CR8","unstructured":"Duvenaud, D., et al.: Convolutional networks on graphs for learning molecular fingerprints (2015)"},{"key":"17_CR9","unstructured":"Dwivedi, V.P., Joshi, C.K., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks (2020)"},{"key":"17_CR10","unstructured":"Ganea, O., Becigneul, G., Hofmann, T.: Hyperbolic neural networks. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 5345\u20135355. Curran Associates, Inc. (2018)"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Gaudet, C.J., Maida, A.S.: Deep quaternion networks. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138 (2018)","DOI":"10.1109\/IJCNN.2018.8489651"},{"key":"17_CR12","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1263\u20131272. PMLR, International Convention Centre, Sydney, Australia, 06\u201311 Aug 2017"},{"key":"17_CR13","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"17_CR14","unstructured":"Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data (2015)"},{"key":"17_CR15","unstructured":"Hu, W., et al.: Open graph benchmark: Datasets for machine learning on graphs (2020)"},{"key":"17_CR16","unstructured":"Hu, W., et al.: Strategies for pre-training graph neural networks. In: International Conference on Learning Representations (2020)"},{"key":"17_CR17","unstructured":"Kipf, T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks. In: Proceedings of the 5th International Conference on Learning Representations, ICLR 2017 (2017)"},{"key":"17_CR18","unstructured":"Kong, K., et al.: Flag: Adversarial data augmentation for graph neural networks (2020)"},{"key":"17_CR19","unstructured":"Li, G., Xiong, C., Thabet, A., Ghanem, B.: DeeperGCN: All you need to train deeper GCNs (2020)"},{"key":"17_CR20","unstructured":"Liu, Q., Nickel, M., Kiela, D.: Hyperbolic graph neural networks. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8230\u20138241. Curran Associates, Inc. (2019)"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Montanari, F., Kuhnke, L., Ter Laak, A., Clevert, D.A.: Modeling physico-chemical ADMET endpoints with multitask graph convolutional networks. Molecules 25(1) 44 (2020)","DOI":"10.3390\/molecules25010044"},{"key":"17_CR22","unstructured":"Nguyen, D.Q., Nguyen, T.D., Phung, D.: Quaternion graph neural networks (2020)"},{"key":"17_CR23","unstructured":"Parcollet, T., et al.: Quaternion recurrent neural networks. In: International Conference on Learning Representations (2019)"},{"key":"17_CR24","unstructured":"Sohoni, N.S., Aberger, C.R., Leszczynski, M., Zhang, J., R\u00e9, C.: Low-memory neural network training: A technical report (2019)"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Tay, Y., et al.: Lightweight and efficient neural natural language processing with quaternion networks (2019)","DOI":"10.18653\/v1\/P19-1145"},{"key":"17_CR26","unstructured":"Trabelsi, C., et al.: Deep complex networks. In: International Conference on Learning Representations (2018)"},{"key":"17_CR27","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"17_CR28","unstructured":"Weisfeiler, B.A.L.A.: A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia 2(9), 12\u201316 (1968)"},{"issue":"1","key":"17_CR29","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"17_CR30","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2019)"},{"key":"17_CR31","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.i., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 5453\u20135462. PMLR (2018)"},{"key":"17_CR32","unstructured":"Zhang, A., et al.: Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with \\$1\/n\\$ parameters. In: International Conference on Learning Representations (2021)"},{"key":"17_CR33","unstructured":"Zhou, J., et al.: Graph neural networks: A review of methods and applications (2019)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86365-4_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T11:07:54Z","timestamp":1631272074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86365-4_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863647","9783030863654"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86365-4_17","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":"7 September 2021","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":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","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":"14 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":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"2.5","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":"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)"}}]}}