{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T14:25:33Z","timestamp":1743776733778,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031359941"},{"type":"electronic","value":"9783031359958"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-35995-8_28","type":"book-chapter","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T07:02:24Z","timestamp":1687935744000},"page":"393-405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Graph-Level Representations Using Ensemble-Based Readout Functions"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7386-5150","authenticated-orcid":false,"given":"Jakub","family":"Binkowski","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3142-4028","authenticated-orcid":false,"given":"Albert","family":"Sawczyn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1859-9093","authenticated-orcid":false,"given":"Denis","family":"Janiak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1487-2569","authenticated-orcid":false,"given":"Piotr","family":"Bielak","sequence":"additional","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":[[2023,6,26]]},"reference":[{"key":"28_CR1","doi-asserted-by":"publisher","unstructured":"Borgwardt, K.M., Ong, C.S., Sch\u00f6nauer, S., Vishwanathan, S.V.N., Smola, A.J., Kriegel, H.P.: Protein function prediction via graph kernels. Bioinform. (Oxford Engl.) 21(Suppl 1), i47\u201356 (2005). https:\/\/doi.org\/10.1093\/bioinformatics\/bti1007","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"28_CR2","doi-asserted-by":"publisher","unstructured":"Bresson, X., Laurent, T.: A Two-step graph convolutional decoder for molecule generation. arXiv, Vancouver, Canada (2019). https:\/\/doi.org\/10.48550\/arXiv.1906.03412, arXiv:1906.03412 [cs, stat]","DOI":"10.48550\/arXiv.1906.03412"},{"key":"28_CR3","doi-asserted-by":"publisher","unstructured":"Bronstein, M.M., Bruna, J., Cohen, T., Veli\u010dkovi\u0107, P.: Geometric deep learning: grids, groups, graphs, geodesics, and gauges (2021). https:\/\/doi.org\/10.48550\/arXiv.2104.13478, arXiv:2104.13478 [cs, stat]","DOI":"10.48550\/arXiv.2104.13478"},{"key":"28_CR4","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv (2014). arXiv:1312.6203 [cs]"},{"key":"28_CR5","unstructured":"Buterez, D., Janet, J.P., Kiddle, S.J., Oglic, D., Li\u00f2, P.: Graph neural networks with adaptive readouts (2022). https:\/\/openreview.net\/forum?id=yts7fLpWY9G"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches (2014). arXiv:1409.1259 [cs, stat]","DOI":"10.3115\/v1\/W14-4012"},{"key":"28_CR7","unstructured":"Corso, G., Cavalleri, L., Beaini, D., Li\u00f2, P., Velickovic, P.: Principal neighbourhood aggregation for graph nets (2020)"},{"key":"28_CR8","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. arXiv (2017). arXiv:1606.09375 [cs, stat]"},{"key":"28_CR9","unstructured":"Dwivedi, V.P., Joshi, C.K., Luu, A.T., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. J. Mach. Learn. Res. 23 (2022). arXiv:2003.00982 [cs, stat]"},{"key":"28_CR10","doi-asserted-by":"publisher","unstructured":"Falcon, W., team, T.P.L.: PyTorch lightning (2019). https:\/\/doi.org\/10.5281\/zenodo.7545285, https:\/\/zenodo.org\/record\/7545285","DOI":"10.5281\/zenodo.7545285"},{"key":"28_CR11","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. arXiv (2019). arXiv:1903.02428 [cs, stat]"},{"key":"28_CR12","doi-asserted-by":"publisher","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, vol. 70. arXiv, Sydney (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.01212, arXiv:1704.01212 [cs]","DOI":"10.48550\/arXiv.1704.01212"},{"key":"28_CR13","doi-asserted-by":"publisher","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). https:\/\/doi.org\/10.48550\/arXiv.1502.03167, arXiv:1502.03167 [cs]","DOI":"10.48550\/arXiv.1502.0316"},{"key":"28_CR14","doi-asserted-by":"publisher","unstructured":"Khasahmadi, A.H., Hassani, K., Moradi, P., Lee, L., Morris, Q.: Memory-based graph networks. arXiv (2020). https:\/\/doi.org\/10.48550\/arXiv.2002.09518, arXiv:2002.09518 [cs, stat]","DOI":"10.48550\/arXiv.2002.09518"},{"key":"28_CR15","doi-asserted-by":"publisher","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv (2017). https:\/\/doi.org\/10.48550\/arXiv.1609.02907, arXiv:1609.02907 [cs, stat]","DOI":"10.48550\/arXiv.1609.02907"},{"key":"28_CR16","unstructured":"Kriege, N., Mutzel, P.: Subgraph matching kernels for attributed graphs. In: Proceedings of the 29th International Conference on International Conference on Machine Learning. Omnipress, Edinburgh (2012)"},{"key":"28_CR17","doi-asserted-by":"publisher","unstructured":"Lee, J., Lee, Y., Kim, J., Kosiorek, A.R., Choi, S., Teh, Y.W.: Set transformer: a framework for attention-based permutation-invariant neural networks. In: Proceedings of the 36th International Conference on Machine Learning, pp. 3744\u20133753. arXiv (2019). https:\/\/doi.org\/10.48550\/arXiv.1810.00825, arXiv:1810.00825 [cs, stat]","DOI":"10.48550\/arXiv.1810.00825"},{"key":"28_CR18","doi-asserted-by":"publisher","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization (2019). https:\/\/doi.org\/10.48550\/arXiv.1711.05101, arXiv:1711.05101 [cs, math]","DOI":"10.48550\/arXiv.1711.05101"},{"key":"28_CR19","unstructured":"Mesquita, D., Souza, A.H., Kaski, S.: Rethinking pooling in graph neural networks (2020). arXiv:2010.11418 [cs]"},{"key":"28_CR20","doi-asserted-by":"publisher","unstructured":"O\u2019Boyle, N.M.: Towards a Universal SMILES representation - a standard method to generate canonical SMILES based on the InChI. J. Cheminform. 4(1), 22 (2012). https:\/\/doi.org\/10.1186\/1758-2946-4-22","DOI":"10.1186\/1758-2946-4-22"},{"key":"28_CR21","doi-asserted-by":"publisher","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library (2019). https:\/\/doi.org\/10.48550\/arXiv.1912.01703, arXiv:1912.01703 [cs, stat]","DOI":"10.48550\/arXiv.1912.01703"},{"key":"28_CR22","doi-asserted-by":"publisher","unstructured":"Rhee, S., Seo, S., Kim, S.: Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification (2018). https:\/\/doi.org\/10.48550\/arXiv.1711.05859, arXiv:1711.05859 [cs]","DOI":"10.48550\/arXiv.1711.05859"},{"key":"28_CR23","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014). http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html"},{"key":"28_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need (2017). arXiv:1706.03762 [cs]"},{"key":"28_CR25","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks (2018). arXiv:1710.10903 [cs, stat]"},{"key":"28_CR26","doi-asserted-by":"publisher","unstructured":"Vinyals, O., Bengio, S., Kudlur, M.: Order matters: sequence to sequence for sets (2016). https:\/\/doi.org\/10.48550\/arXiv.1511.06391, arXiv:1511.06391 [cs, stat]","DOI":"10.48550\/arXiv.1511.06391"},{"key":"28_CR27","unstructured":"Wagstaff, E., Fuchs, F.B., Engelcke, M., Posner, I., Osborne, M.: On the limitations of representing functions on sets (2019). arXiv:1901.09006 [cs, stat]"},{"key":"28_CR28","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? (2019). arXiv:1810.00826 [cs, stat]"},{"key":"28_CR29","doi-asserted-by":"publisher","unstructured":"Yanardag, P., Vishwanathan, S.: Deep graph kernels. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, pp. 1365\u20131374. Association for Computing Machinery, New York (2015). https:\/\/doi.org\/10.1145\/2783258.2783417","DOI":"10.1145\/2783258.2783417"},{"key":"28_CR30","doi-asserted-by":"publisher","unstructured":"Ying, R., You, J., Morris, C., Ren, X., Hamilton, W.L., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling (2019). https:\/\/doi.org\/10.48550\/arXiv.1806.08804, arXiv:1806.08804 [cs, stat]","DOI":"10.48550\/arXiv.1806.08804"},{"key":"28_CR31","doi-asserted-by":"publisher","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R., Smola, A.: Deep sets (2018). https:\/\/doi.org\/10.48550\/arXiv.1703.06114, arXiv:1703.06114 [cs, stat]","DOI":"10.48550\/arXiv.1703.06114"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35995-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T14:06:10Z","timestamp":1691071570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35995-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031359941","9783031359958"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35995-8_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 June 2023","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":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 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":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","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":"188","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":"94","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":"35% - 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":"2,8","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":"3,2","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)"}}]}}