{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:36:04Z","timestamp":1763202964375,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031746420"},{"type":"electronic","value":"9783031746437"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-74643-7_3","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T23:22:03Z","timestamp":1735687323000},"page":"25-36","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Non-dissipative Propagation by\u00a0Randomized Anti-symmetric Deep Graph Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5526-2479","authenticated-orcid":false,"given":"Alessio","family":"Gravina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6692-2564","authenticated-orcid":false,"given":"Claudio","family":"Gallicchio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5213-2468","authenticated-orcid":false,"given":"Davide","family":"Bacciu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"3_CR1","unstructured":"Alon, U., Yahav, E.: On the bottleneck of graph neural networks and its practical implications. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=i80OPhOCVH2"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Ascher, U., Mattheij, R., Russell, R.: Numerical solution of boundary value problems for ordinary differential equations. In: Classics in Applied Mathematics. Society for Industrial and Applied Mathematics (SIAM) (1995)","DOI":"10.1137\/1.9781611971231"},{"key":"3_CR3","unstructured":"Chamberlain, B., Rowbottom, J., Gorinova, M.I., Bronstein, M., Webb, S., Rossi, E.: Grand: graph neural diffusion. In: International Conference on Machine Learning, pp. 1407\u20131418. PMLR (2021)"},{"key":"3_CR4","unstructured":"Chang, B., Chen, M., Haber, E., Chi, E.H.: AntisymmetricRNN: a dynamical system view on recurrent neural networks. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=ryxepo0cFX"},{"key":"3_CR5","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a0119, pp. 1725\u20131735. PMLR (2020). https:\/\/proceedings.mlr.press\/v119\/chen20v.html"},{"key":"3_CR6","unstructured":"Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. Adv. Neural Inf. Process. Syst. (2018)"},{"key":"3_CR7","unstructured":"Corso, G., Cavalleri, L., Beaini, D., Li\u00f2, P., Veli\u010dkovi\u0107, P.: Principal neighbourhood aggregation for graph nets. Adv. Neural Inf. Process. Syst. (2020)"},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Gallicchio, C., Micheli, A.: Fast and deep graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 3898\u20133905 (2020). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/5803","DOI":"10.1609\/aaai.v34i04.5803"},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Gallicchio, C., Scardapane, S.: Deep randomized neural networks, pp. 43\u201368. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-43883-8_3","DOI":"10.1007\/978-3-030-43883-8_3"},{"key":"3_CR10","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 1263\u20131272. JMLR.org (2017)"},{"key":"3_CR11","unstructured":"Gravina, A., Bacciu, D., Gallicchio, C.: Anti-Symmetric DGN: a stable architecture for deep graph networks. In: The Eleventh International Conference on Learning Representations (2023). https:\/\/openreview.net\/forum?id=J3Y7cgZOOS"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. CoRR arxiv:1705.03341 (2017)","DOI":"10.1088\/1361-6420\/aa9a90"},{"key":"3_CR13","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS (2017)"},{"key":"3_CR14","doi-asserted-by":"publisher","unstructured":"Iyer, R.G., Wang, W., Sun, Y.: Bi-level attention graph neural networks. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 1126\u20131131 (2021). https:\/\/doi.org\/10.1109\/ICDM51629.2021.00133","DOI":"10.1109\/ICDM51629.2021.00133"},{"key":"3_CR15","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"3_CR16","unstructured":"Poli, M., Massaroli, S., Park, J., Yamashita, A., Asama, H., Park, J.: Graph neural ordinary differential equations. arXiv preprint arXiv:1911.07532 (2019)"},{"issue":"1","key":"3_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s43588-021-00184-y","volume":"2","author":"CD Schuman","year":"2022","unstructured":"Schuman, C.D., Kulkarni, S.R., Parsa, M., Mitchell, J.P., Date, P., Kay, B.: Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2(1), 10\u201319 (2022). https:\/\/doi.org\/10.1038\/s43588-021-00184-y","journal-title":"Nat. Comput. Sci."},{"key":"3_CR18","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). https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"issue":"2","key":"3_CR19","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s42256-023-00609-5","volume":"5","author":"S Wang","year":"2023","unstructured":"Wang, S., et al.: Echo state graph neural networks with analogue random resistive memory arrays. Nat. Mach. Intell. 5(2), 104\u2013113 (2023). https:\/\/doi.org\/10.1038\/s42256-023-00609-5","journal-title":"Nat. Mach. Intell."},{"key":"3_CR20","doi-asserted-by":"publisher","unstructured":"Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, WWW \u201919, pp. 2022\u20132032. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"3_CR21","unstructured":"Wang, Y., Wang, Y., Yang, J., Lin, Z.: Dissecting the diffusion process in linear graph convolutional networks. CoRR arxiv:2102.10739 (2021)"},{"issue":"1","key":"3_CR22","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). https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"3_CR23","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019). https:\/\/openreview.net\/forum?id=ryGs6iA5Km"}],"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\/978-3-031-74643-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T02:32:30Z","timestamp":1735698750000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74643-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746420","9783031746437"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74643-7_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","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":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}