{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:53:29Z","timestamp":1743026009576,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031165634"},{"type":"electronic","value":"9783031165641"}],"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\/978-3-031-16564-1_35","type":"book-chapter","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:02:43Z","timestamp":1664146963000},"page":"368-377","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Convolutional Networks Using Node Addition and\u00a0Edge Reweighting"],"prefix":"10.1007","author":[{"given":"Wen-Yu","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"35_CR1","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and deep locally connected networks on graphs. In: Proceedings of International Conference on Learning Representations (2014)"},{"key":"35_CR2","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of International Conference on Neural Information Processing Systems, pp. 3844\u20133852 (2016)"},{"key":"35_CR3","unstructured":"Duvenaud, D., et al.: Convolutional networks on graphs for learning molecular fingerprints. In: Proceedings of International Conference on Neural Information Processing Systems, pp. 2224\u20132232 (2015)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., V\u00e1zquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional DenseNets for semantic segmentation. In: Proceedings of International Workshop on Computer Vision in Vehicle Technology (2017)","DOI":"10.1109\/CVPRW.2017.156"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Jiang, B., Zhang, Z., Lin, D., Tang, J., Luo, B.: Semi-supervised learning with graph learning-convolutional networks. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11313\u201311320 (2019)","DOI":"10.1109\/CVPR.2019.01157"},{"key":"35_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: arXiv preprint arXiv:1412.6980 (2014)"},{"key":"35_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of International Conference on Learning Representations (2017)"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhou, Q., Qiang, Y., Kang, B., Wu, X., Zheng, B.: FDDWNet: a lightweight convolutional neural network for real-time semantic segmentation. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2373\u20132377 (2020)","DOI":"10.1109\/ICASSP40776.2020.9053838"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Manandhar, D., Yap, K.H., Bastan, M., Heng, Z.: Brand-aware fashion clothing search using CNN feature encoding and re-ranking. In: Proceedings of the IEEE International Symposium on Circuits and Systems, pp. 1\u20135 (2018)","DOI":"10.1109\/ISCAS.2018.8351401"},{"key":"35_CR11","unstructured":"Namata, G., London, B., Getoor, L., Huang, B.: Query-driven active surveying for collective classification. In: Proceedings of International Workshop on Mining and Learning with Graphs (2012)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 977\u2013986 (2014)","DOI":"10.1145\/2623330.2623726"},{"key":"35_CR13","unstructured":"Paszke, A., et al.: Automatic differentiation in pytorch. In: Proceedings of NIPS Workshop on Autodiff (2017)"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Christiansen, H., Azawi, N.H.: Efficient and precise classification of CT scannings of renal tumors using convolutional neural networks. In: Proceedings of Foundations of Intelligent Systems: 25th International Symposium, pp. 440\u2013447 (2020)","DOI":"10.1007\/978-3-030-59491-6_42"},{"key":"35_CR15","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(85), 2825\u20132830 (2011)"},{"key":"35_CR16","unstructured":"AssertionError in assert not torch.isnan(h_prime).any(). https:\/\/github.com\/Diego999\/pyGAT\/issues\/11 (2018)"},{"issue":"7","key":"35_CR17","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1109\/TPAMI.2018.2846566","volume":"41","author":"F Radenovi\u0107","year":"2019","unstructured":"Radenovi\u0107, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1655\u20131668 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR18","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., G\u00fcnnemann, S.: Pitfalls of graph neural network evaluation. In: arXiv preprint arXiv:1811.05868 (2018)"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Valem, L.P., Pedronette, D.C.G.: A denoising convolutional neural network for self-supervised rank effectiveness estimation on image retrieval. In: Proceedings of the International Conference on Multimedia Retrieval, pp. 294\u2013302 (2021)","DOI":"10.1145\/3460426.3463645"},{"key":"35_CR20","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: Proceedings of International Conference on Learning Representations (2018)"},{"key":"35_CR21","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: DropEdge: towards deep graph convolutional networks on node classification. In: Proceedings of International Conference on Learning Representations (2020)"},{"key":"35_CR22","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: arXiv preprint arXiv:1603.08861 (2016)"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Yu, D., Zhang, R., Jiang, Z., Wu, Y., Yang, Y.: Graph-revised convolutional network. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2020)","DOI":"10.1007\/978-3-030-67664-3_23"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhang, H., He, X., Chen, X., Xiong, L., Qin, Z.: Aesthetic-based clothing recommendation. In: Proceedings of the World Wide Web Conference, pp. 649\u2013658 (2018)","DOI":"10.1145\/3178876.3186146"},{"key":"35_CR25","unstructured":"Zhou, D., Bousquet, Q., Lal, T.N., Weston, J., Sch$$\\ddot{o}$$lkopf, B.: Learning with local and global consistency. In: Proceedings of NIPS Foundation Advances in Neural Information Processing Systems, pp. 321\u2013328 (2003)"},{"key":"35_CR26","unstructured":"Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of International Conference on Machine Learning, pp. 912\u2013919 (2003)"}],"container-title":["Lecture Notes in Computer Science","Foundations of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16564-1_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:27:12Z","timestamp":1710336432000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16564-1_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031165634","9783031165641"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16564-1_35","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":"26 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISMIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Methodologies for Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cosenza","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ismis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ismis2022.icar.cnr.it\/","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":"71","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":"31","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":"11","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":"44% - 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":"3.7","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":"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)"}},{"value":"Number and type of other papers accepted :\t4 industrial papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}