{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T03:15:56Z","timestamp":1743045356784,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031087509"},{"type":"electronic","value":"9783031087516"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-08751-6_13","type":"book-chapter","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T06:03:15Z","timestamp":1655791395000},"page":"178-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Retrofitting Structural Graph Embeddings with\u00a0Node Attribute Information"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1487-2569","authenticated-orcid":false,"given":"Piotr","family":"Bielak","sequence":"first","affiliation":[]},{"given":"Daria","family":"Puchalska","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":[[2022,6,15]]},"reference":[{"unstructured":"Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs. https:\/\/github.com\/benedekrozemberczki\/karateclub","key":"13_CR1"},{"unstructured":"PyTorch geometric main page. https:\/\/pytorch-geometric.readthedocs.io\/en\/latest\/index.html","key":"13_CR2"},{"unstructured":"Bandyopadhyay, S., Kara, H., Kannan, A., Murty, M.: FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks, April 2018","key":"13_CR3"},{"key":"13_CR4","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135\u2013146 (2017)","journal-title":"Trans. Assoc. Comput. Linguist."},{"doi-asserted-by":"publisher","unstructured":"Gao, H., Huang, H.: Deep attributed network embedding. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pp. 3364\u20133370 (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/467","key":"13_CR5","DOI":"10.24963\/ijcai.2018\/467"},{"doi-asserted-by":"publisher","unstructured":"Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 855\u2013864. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939754","key":"13_CR6","DOI":"10.1145\/2939672.2939754"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","key":"13_CR7","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)","key":"13_CR8"},{"doi-asserted-by":"publisher","unstructured":"McAuley, J., Targett, C., Shi, Q., van den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43\u201352. Association for Computing Machinery (2015). https:\/\/doi.org\/10.1145\/2766462.2767755","key":"13_CR9","DOI":"10.1145\/2766462.2767755"},{"unstructured":"Mernyei, P., Cangea, C.: Wiki-CS: a wikipedia-based benchmark for graph neural networks. arXiv preprint arXiv:2007.02901 (2020)","key":"13_CR10"},{"unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Bengio, Y., LeCun, Y. (eds.) 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, 2\u20134 May 2013, Workshop Track Proceedings (2013)","key":"13_CR11"},{"doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 701\u2013710. ACM (2014)","key":"13_CR12","DOI":"10.1145\/2623330.2623732"},{"doi-asserted-by":"crossref","unstructured":"Sinha, A., et al.: An overview of microsoft academic service (MAS) and applications. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 243\u2013246. Association for Computing Machinery, New York (2015). https:\/\/doi.org\/10.1145\/2740908.2742839","key":"13_CR13","DOI":"10.1145\/2740908.2742839"},{"unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 97, pp. 6105\u20136114. PMLR, 09\u201315 June 2019","key":"13_CR14"},{"doi-asserted-by":"publisher","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, May 2015. https:\/\/doi.org\/10.1145\/2736277.2741093. http:\/\/dx.doi.org\/10.1145\/2736277.2741093","key":"13_CR15","DOI":"10.1145\/2736277.2741093"},{"doi-asserted-by":"publisher","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 1225\u20131234. Association for Computing Machinery, New York (2016). https:\/\/doi.org\/10.1145\/2939672.2939753","key":"13_CR16","DOI":"10.1145\/2939672.2939753"},{"unstructured":"Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI 2015, pp. 2111\u20132117. AAAI Press (2015)","key":"13_CR17"},{"doi-asserted-by":"publisher","unstructured":"Zhang, Z., et al.: ANRL: attributed network representation learning via deep neural networks. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, pp. 3155\u20133161. International Joint Conferences on Artificial Intelligence Organization, July 2018. https:\/\/doi.org\/10.24963\/ijcai.2018\/438","key":"13_CR18","DOI":"10.24963\/ijcai.2018\/438"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08751-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T06:04:49Z","timestamp":1655791489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08751-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031087509","9783031087516"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08751-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 June 2022","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":"London","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2022\/","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":"474","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":"175","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":"78","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":"37% - 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","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)"}}]}}