{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T23:00:11Z","timestamp":1772319611112,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031637773","type":"print"},{"value":"9783031637780","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-63778-0_1","type":"book-chapter","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T10:01:27Z","timestamp":1719655287000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Representation Learning in\u00a0Multiplex Graphs: Where and\u00a0How to\u00a0Fuse Information?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1487-2569","authenticated-orcid":false,"given":"Piotr","family":"Bielak","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8417-1012","authenticated-orcid":false,"given":"Tomasz","family":"Kajdanowicz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109631","volume":"256","author":"P Bielak","year":"2022","unstructured":"Bielak, P., Kajdanowicz, T., Chawla, N.V.: Graph Barlow twins: a self-supervised representation learning framework for graphs. Knowl.-Based Syst. 256, 109631 (2022). https:\/\/doi.org\/10.1016\/j.knosys.2022.109631","journal-title":"Knowl.-Based Syst."},{"key":"1_CR2","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch Geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: WWW 2016 (2016). https:\/\/doi.org\/10.1145\/2872427.2883037","DOI":"10.1145\/2872427.2883037"},{"key":"1_CR4","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. In: NIPS Workshop on Bayesian Deep Learning (2016)"},{"key":"1_CR5","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Kuprieiev, R., et al.: DVC: data version control - git for data & models, May 2021. https:\/\/doi.org\/10.5281\/zenodo.4733984","DOI":"10.5281\/zenodo.4733984"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Mo, Y., Chen, Y., Peng, L., Shi, X., Zhu, X.: Simple self-supervised multiplex graph representation learning. In: Proceedings of the 30th ACM International Conference on Multimedia, MM 2022 (2022). https:\/\/doi.org\/10.1145\/3503161.3547949","DOI":"10.1145\/3503161.3547949"},{"key":"1_CR8","doi-asserted-by":"publisher","unstructured":"Park, C., Kim, D., Han, J., Yu, H.: Unsupervised attributed multiplex network embedding. In: AAAI 2020 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.5985","DOI":"10.1609\/aaai.v34i04.5985"},{"key":"1_CR9","doi-asserted-by":"publisher","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD 2014 (2014). https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"1_CR10","unstructured":"Ren, Y., Liu, B., Huang, C., Dai, P., Bo, L., Zhang, J.: Heterogeneous deep graph infomax. ArXiv abs\/1911.08538 (2019)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: WWW. ACM (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"1_CR12","unstructured":"Thakoor, S., Tallec, C., Azar, M.G., Munos, R., Veli\u010dkovi\u0107, P., Valko, M.: Bootstrapped representation learning on graphs. In: ICLR 2021 Workshop on Geometrical and Topological Representation Learning (2021)"},{"key":"1_CR13","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":"1_CR14","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: International Conference on Learning Representations (2019)"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"Wang, X., et al.: Heterogeneous graph attention network. In: WWW 2019 (2019). https:\/\/doi.org\/10.1145\/3308558.3313562","DOI":"10.1145\/3308558.3313562"},{"key":"1_CR16","doi-asserted-by":"publisher","unstructured":"Wang, X., Liu, N., Han, H., Shi, C.: Self-supervised heterogeneous graph neural network with co-contrastive learning. In: KDD 2021 (2021). https:\/\/doi.org\/10.1145\/3447548.3467415","DOI":"10.1145\/3447548.3467415"},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Wu, C.Y., Beutel, A., Ahmed, A., Smola, A.J.: Explaining reviews and ratings with PACO: poisson additive co-clustering. In: WWW 2016 (2016). https:\/\/doi.org\/10.1145\/2872518.2889400","DOI":"10.1145\/2872518.2889400"},{"key":"1_CR18","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. In: NeurIPS 2020 (2020)"},{"key":"1_CR19","doi-asserted-by":"publisher","unstructured":"Yu, P., Fu, C., Yu, Y., Huang, C., Zhao, Z., Dong, J.: Multiplex heterogeneous graph convolutional network. In: KDD 2022 (2022). https:\/\/doi.org\/10.1145\/3534678.3539482","DOI":"10.1145\/3534678.3539482"},{"key":"1_CR20","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. In: ICML Workshop on Graph Representation Learning and Beyond (2020)"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63778-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T10:02:36Z","timestamp":1727949756000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63778-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031637773","9783031637780"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63778-0_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"30 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malaga","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}