{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:43:33Z","timestamp":1759495413761,"version":"build-2065373602"},"publisher-location":"Berlin, Heidelberg","reference-count":44,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"value":"9783662722428","type":"print"},{"value":"9783662722435","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"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":[[2026]]},"DOI":"10.1007\/978-3-662-72243-5_8","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:14:16Z","timestamp":1759493656000},"page":"130-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-class and\u00a0Multi-task Strategies for\u00a0Neural Directed Link Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1274-6937","authenticated-orcid":false,"given":"Claudio","family":"Moroni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6071-8247","authenticated-orcid":false,"given":"Claudio","family":"Borile","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9160-7523","authenticated-orcid":false,"given":"Carolina","family":"Mattsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9161-5339","authenticated-orcid":false,"given":"Michele","family":"Starnini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-0374","authenticated-orcid":false,"given":"Andr\u00e9","family":"Panisson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"issue":"1","key":"8_CR1","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1103\/RevModPhys.74.47","volume":"74","author":"R Albert","year":"2002","unstructured":"Albert, R., Barab\u00e1si, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)","journal-title":"Rev. Mod. Phys."},{"key":"8_CR2","unstructured":"Altman, E., Blanu\u0161a, J., Von\u00a0Niederh\u00e4usern, L., Egressy, B., Anghel, A., Atasu, K.: Realistic synthetic financial transactions for anti-money laundering models. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"8_CR3","doi-asserted-by":"crossref","unstructured":"Arrar, D., Kamel, N., Lakhfif, A.: A comprehensive survey of link prediction methods. J. Supercomput. (2023)","DOI":"10.1007\/s11227-023-05591-8"},{"key":"8_CR4","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol.\u00a026 (2013)"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Cai, L., Ji, S.: A multi-scale approach for graph link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 3308\u20133315 (2020)","DOI":"10.1609\/aaai.v34i04.5731"},{"issue":"5","key":"8_CR6","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.crma.2012.03.014","volume":"350","author":"JA D\u00e9sid\u00e9ri","year":"2012","unstructured":"D\u00e9sid\u00e9ri, J.A.: Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. C. R. Math. 350(5), 313\u2013318 (2012)","journal-title":"C. R. Math."},{"key":"8_CR7","unstructured":"Dong, K., Guo, Z., Chawla, N.: Pure message passing can estimate common neighbor for link prediction. In: Advances in Neural Information Processing Systems, vol. 37, pp. 73000\u201373035 (2024)"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Egressy, B., Von\u00a0Niederh\u00e4usern, L., Blanu\u0161a, J., Altman, E., Wattenhofer, R., Atasu, K.: Provably powerful graph neural networks for directed multigraphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a038, pp. 11838\u201311846 (2024)","DOI":"10.1609\/aaai.v38i10.29069"},{"key":"8_CR9","doi-asserted-by":"crossref","unstructured":"Fu, X., Zhang, J., Meng, Z., King, I.: MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: WWW 2020, pp. 2331\u20132341. Association for Computing Machinery, New York (2020)","DOI":"10.1145\/3366423.3380297"},{"key":"8_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 (2017)"},{"key":"8_CR11","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"128036","DOI":"10.1016\/j.physa.2022.128036","volume":"605","author":"C He","year":"2022","unstructured":"He, C., Zeng, J., Li, Y., Liu, S., Liu, L., Xiao, C.: Two-stream signed directed graph convolutional network for link prediction. Phys. A: Stat. Mech. Appl. 605, 128036 (2022)","journal-title":"Phys. A: Stat. Mech. Appl."},{"key":"8_CR13","doi-asserted-by":"publisher","first-page":"110589","DOI":"10.1016\/j.knosys.2023.110589","volume":"272","author":"C He","year":"2023","unstructured":"He, C., Cheng, J., Fei, X., Weng, Y., Zheng, Y., Tang, Y.: Community preserving adaptive graph convolutional networks for link prediction in attributed networks. Knowl.-Based Syst. 272, 110589 (2023)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"8_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan, A., et al.: Knowledge graphs. ACM Comput. Surv. (CSUR) 54(4), 1\u201337 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Holme, P., Saram\u00e4ki, J.: Temporal Network Theory. Springer (2019)","DOI":"10.1007\/978-3-030-23495-9"},{"key":"8_CR16","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. In: Advances in Neural Information Processing Systems, vol. 33, pp. 22118\u201322133 (2020)"},{"key":"8_CR17","unstructured":"Hu, Y., Xian, R., Wu, Q., Fan, Q., Yin, L., Zhao, H.: Revisiting scalarization in multi-task learning: a theoretical perspective. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Jin, M., et al.: A survey on graph neural networks for time series: forecasting, classification, imputation, and anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3443141"},{"key":"8_CR19","unstructured":"Johannessen, F., Jullum, M.: Finding money launderers using heterogeneous graph neural networks. arXiv preprint arXiv:2307.13499 (2023)"},{"key":"8_CR20","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Kollias, G., Kalantzis, V., Id\u2019e, T., Lozano, A.C., Abe, N.: Directed graph auto-encoders. In: AAAI Conference on Artificial Intelligence (2022)","DOI":"10.1609\/aaai.v36i7.20682"},{"key":"8_CR22","doi-asserted-by":"publisher","first-page":"124289","DOI":"10.1016\/j.physa.2020.124289","volume":"553","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Singh, S.S., Singh, K., Biswas, B.: Link prediction techniques, applications, and performance: a survey. Phy. A: Stat. Mech. Appl. 553, 124289 (2020)","journal-title":"Phy. A: Stat. Mech. Appl."},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Li, J., et al.: What\u2019s behind the mask: understanding masked graph modeling for graph autoencoders. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1268\u20131279 (2023)","DOI":"10.1145\/3580305.3599546"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, H.: Recommendation as link prediction: a graph kernel-based machine learning approach. In: Proceedings of the 9th ACM\/IEEE-CS Joint Conference on Digital Libraries, JCDL 2009, pp. 213\u2013216. ACM, New York (2009)","DOI":"10.1145\/1555400.1555433"},{"key":"8_CR25","doi-asserted-by":"publisher","first-page":"127504","DOI":"10.1016\/j.physa.2022.127504","volume":"600","author":"D Lin","year":"2022","unstructured":"Lin, D., Wu, J., Xuan, Q., Tse, C.K.: Ethereum transaction tracking: inferring evolution of transaction networks via link prediction. Phys. A: Stat. Mech. Appl. 600, 127504 (2022)","journal-title":"Phys. A: Stat. Mech. Appl."},{"issue":"6","key":"8_CR26","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1016\/j.physa.2010.11.027","volume":"390","author":"L L\u00fc","year":"2011","unstructured":"L\u00fc, L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A: Stat. Mech. Appl. 390(6), 1150\u20131170 (2011)","journal-title":"Phys. A: Stat. Mech. Appl."},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Qin, M., Yeung, D.Y.: Temporal link prediction: a unified framework, taxonomy, and review. ACM Comput. Surv. 56(4) (2023)","DOI":"10.1145\/3625820"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Reiser, P., et al.: Graph neural networks for materials science and chemistry. Commun. Mater. 3(1) (2022)","DOI":"10.1038\/s43246-022-00315-6"},{"key":"8_CR29","unstructured":"Ren, H., Hu, W., Leskovec, J.: Query2box: reasoning over knowledge graphs in vector space using box embeddings. arXiv preprint arXiv:2002.05969 (2020)"},{"key":"8_CR30","unstructured":"Rossi, E., Charpentier, B., Giovanni, F.D., Frasca, F., G\u00fcnnemann, S., Bronstein, M.M.: Edge directionality improves learning on heterophilic graphs. In: Proceedings of the Second Learning on Graphs Conference, pp. 25:1\u201325:27. PMLR (2024). iSSN: 2640-3498"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Salha, G., Limnios, S., Hennequin, R., Tran, V.A., Vazirgiannis, M.: Gravity-inspired graph autoencoders for directed link prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 589\u2013598 (2019)","DOI":"10.1145\/3357384.3358023"},{"issue":"77","key":"8_CR32","first-page":"2539","volume":"12","author":"N Shervashidze","year":"2011","unstructured":"Shervashidze, N., Schweitzer, P., Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-Lehman graph kernels. J. Mach. Learn. Res. 12(77), 2539\u20132561 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"8_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/978-3-030-01418-6_41","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"M Simonovsky","year":"2018","unstructured":"Simonovsky, M., Komodakis, N.: GraphVAE: towards generation of small graphs using variational autoencoders. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11139, pp. 412\u2013422. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01418-6_41"},{"key":"8_CR34","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":"8_CR35","doi-asserted-by":"publisher","first-page":"108215","DOI":"10.1016\/j.patcog.2021.108215","volume":"121","author":"J Wang","year":"2022","unstructured":"Wang, J., Liang, J., Yao, K., Liang, J., Wang, D.: Graph convolutional autoencoders with co-learning of graph structure and node attributes. Pattern Recogn. 121, 108215 (2022)","journal-title":"Pattern Recogn."},{"key":"8_CR36","unstructured":"Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019)"},{"issue":"3","key":"8_CR37","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s41019-022-00188-2","volume":"7","author":"H Wu","year":"2022","unstructured":"Wu, H., Song, C., Ge, Y., Ge, T.: Link prediction on complex networks: an experimental survey. Data Sci. Eng. 7(3), 253\u2013278 (2022)","journal-title":"Data Sci. Eng."},{"key":"8_CR38","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2019)"},{"key":"8_CR39","doi-asserted-by":"publisher","first-page":"108241","DOI":"10.1016\/j.knosys.2022.108241","volume":"241","author":"T Yi","year":"2022","unstructured":"Yi, T., Zhang, S., Bu, Z., Du, J., Fang, C.: Link prediction based on higher-order structure extraction and autoencoder learning in directed networks. Knowl.-Based Syst. 241, 108241 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"8_CR40","unstructured":"Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"8_CR41","doi-asserted-by":"publisher","first-page":"109216","DOI":"10.1016\/j.ijar.2024.109216","volume":"172","author":"S Zhang","year":"2024","unstructured":"Zhang, S., Zhang, W., Bu, Z., Zhang, X.: ClusterLP: a novel cluster-aware link prediction model in undirected and directed graphs. Int. J. Approximate Reasoning 172, 109216 (2024)","journal-title":"Int. J. Approximate Reasoning"},{"key":"8_CR42","unstructured":"Zhang, X., He, Y., Brugnone, N., Perlmutter, M., Hirn, M.: MagNet: a neural network for directed graphs. In: Advances in Neural Information Processing Systems (2021)"},{"key":"8_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tan, Y., Jian, S., Wu, Q., Li, K.: DGLP: incorporating orientation information for enhanced link prediction in directed graphs. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6565\u20136569. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10446078"},{"key":"8_CR44","doi-asserted-by":"crossref","unstructured":"Zhu, S., Li, J., Peng, H., Wang, S., He, L.: Adversarial directed graph embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4741\u20134748 (2021)","DOI":"10.1609\/aaai.v35i5.16605"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-72243-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T12:14:35Z","timestamp":1759493675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-662-72243-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"ISBN":["9783662722428","9783662722435"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-72243-5_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"4 October 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":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}