{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:30:57Z","timestamp":1743136257266,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819706686"},{"type":"electronic","value":"9789819706693"}],"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\/978-981-97-0669-3_35","type":"book-chapter","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T21:20:16Z","timestamp":1709155216000},"page":"385-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scalable Deep Metric Learning on\u00a0Attributed Graphs"],"prefix":"10.1007","author":[{"given":"Xiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gagan","family":"Agrawal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruoming","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajiv","family":"Ramnath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., Cui, P.: Structural deep clustering network. In: WWW (2020)","DOI":"10.1145\/3366423.3380214"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Chen, B., Li, P., Yan, Z., Wang, B., Zhang, L.: Deep metric learning with graph consistency. In: AAAI, vol. 35, pp. 982\u2013990 (2021)","DOI":"10.1609\/aaai.v35i2.16182"},{"key":"35_CR3","unstructured":"Chen, M., Wei, Z., Ding, B., Li, Y., Yuan, Y., Du, X., Wen, J.: Scalable graph neural networks via bidirectional propagation. In: NeurIPS (2020)"},{"key":"35_CR4","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML (2020)"},{"key":"35_CR5","unstructured":"Chuang, C.Y., Robinson, J., Lin, Y.C., Torralba, A., Jegelka, S.: Debiased contrastive learning. In: NeurIPS (2020)"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Cui, G., Zhou, J., Yang, C., Liu, Z.: Adaptive graph encoder for attributed graph embedding. In: KDD (2020)","DOI":"10.1145\/3394486.3403140"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Cui, Y., et al.: Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.130"},{"key":"35_CR8","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http:\/\/www.deeplearningbook.org"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Hartigan, J.A., Wong, M.A.: A k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100\u2013108 (1979)","DOI":"10.2307\/2346830"},{"key":"35_CR10","unstructured":"Khosla, P., et al.: Supervised contrastive learning. In: NeurIPS (2020)"},{"key":"35_CR11","unstructured":"Lazer, D., et al.: Life in the network: the coming age of computational social. Science 323 (2009)"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"35_CR13","doi-asserted-by":"crossref","unstructured":"Meyer, B.J., Harwood, B., Drummond, T.: Deep metric learning and image classification with nearest neighbour gaussian kernels. In: ICIP (2018)","DOI":"10.1109\/ICIP.2018.8451297"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23) (2006)","DOI":"10.1073\/pnas.0601602103"},{"key":"35_CR15","unstructured":"Norouzi, M., Fleet, D.J., Salakhutdinov, R.R.: Hamming distance metric learning. In: Advances in Neural Information Processing Systems, vol. 25 (2012)"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Oh Song, H., Xiang, Y., Jegelka, S., Savarese, S.: Deep metric learning via lifted structured feature embedding. In: CVPR, pp. 4004\u20134012 (2016)","DOI":"10.1109\/CVPR.2016.434"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, M., Chang, H., Lee, K., Choi, J.: Symmetric graph convolutional autoencoder for unsupervised graph representation learning. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00662"},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: KDD (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"35_CR20","unstructured":"Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: Advances in Neural Information Processing Systems (2016)"},{"key":"35_CR21","unstructured":"Thomas, K.N., Max, W.: Variational graph auto-encoders. In: NIPS Workshop on Bayesian Deep Learning (2016)"},{"key":"35_CR22","unstructured":"Thomas, K.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"35_CR23","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., Hjelm, R.: Deep graph infomax. In: ICLR (2019)"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, H.: Understanding the behaviour of contrastive loss. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00252"},{"key":"35_CR25","unstructured":"Wang, T., Isola, P.: Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In: ICML (2020)"},{"key":"35_CR26","unstructured":"Xia, J., Wu, L., Wang, G., Chen, J., Li, S.Z.: ProGCL: rethinking hard negative mining in graph contrastive learning. In: ICML (2022)"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: KDD. MDS 2012 (2012)","DOI":"10.1109\/ICDM.2012.138"},{"key":"35_CR28","doi-asserted-by":"crossref","unstructured":"Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: KDD (2018)","DOI":"10.1145\/3219819.3219890"},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liu, H., Li, Q., Wu, X.: Attributed graph clustering via adaptive graph convolution. In: IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/601"},{"key":"35_CR30","doi-asserted-by":"crossref","unstructured":"Zhao, W., Rao, Y., Wang, Z., Lu, J., Zhou, J.: Towards interpretable deep metric learning with structural matching. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (2021)","DOI":"10.1109\/ICCV48922.2021.00974"},{"key":"35_CR31","unstructured":"Zhu, H., Koniusz, P.: Simple spectral graph convolution. In: ICLR (2021)"},{"key":"35_CR32","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: WWW (2021)","DOI":"10.1145\/3442381.3449802"}],"container-title":["Lecture Notes in Computer Science","Computational Data and Social Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-0669-3_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T21:29:00Z","timestamp":1709155740000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-0669-3_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819706686","9789819706693"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-0669-3_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSoNet","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Data and Social Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hanoi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"11 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csonet2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/csonet-conf.github.io\/csonet23\/index.html","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"64","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":"23","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":"14","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":"36% - 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":"2.7","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":"2.0","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":"The four extended abstracts are also included in this proceedings.","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)"}}]}}