{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:24:58Z","timestamp":1743092698950,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031402821"},{"type":"electronic","value":"9783031402838"}],"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\/978-3-031-40283-8_26","type":"book-chapter","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T23:02:48Z","timestamp":1691535768000},"page":"300-314","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Community-Enhanced Contrastive Siamese Networks for\u00a0Graph Representation Learning"],"prefix":"10.1007","author":[{"given":"Yafang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guixiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baokai","family":"Zu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","unstructured":"Zhang, D., Yin, J., Zhu, X., Zhang, C.: Network representation learning: a survey. IEEE Transactions on Big Data, pp. 3\u201328 (2018). https:\/\/doi.org\/10.1109\/tbdata.2018.2850013","DOI":"10.1109\/tbdata.2018.2850013"},{"key":"26_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-319-66182-7_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"SI Ktena","year":"2017","unstructured":"Ktena, S.I., et al.: Distance metric learning using graph convolutional networks: application to\u00a0functional brain networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 469\u2013477. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_54"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Lin, H., Ma, J., Cheng, M., Yang, Z., Chen, L., Chen, G.: Rumor detection on twitter with claim-guided hierarchical graph attention networks. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 10035\u201310047 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.786"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Bastings, J., Titov, I., Aziz, W., Marcheggiani, D., Sima\u2019an, K.: Graph convolutional encoders for syntax-aware neural machine translation. The Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/D17-1209"},{"key":"26_CR5","unstructured":"Ma, J., Zhou, C., Cui, P., Yang, H., Zhu, W.: Learning disentangled representations for a recommendation. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"26_CR6","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 Joint Conference on Artifcial lntelligence, pp. 2111\u20132117 (2015)"},{"key":"26_CR7","unstructured":"Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Guo, X., Gao, L., Liu, X., Yin, J.: Improved deep embedded clustering with local structure preservation. In: IJCAI, pp. 1753\u20131759 (2017)","DOI":"10.24963\/ijcai.2017\/243"},{"key":"26_CR9","unstructured":"Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478\u2013487. PMLR (2016)"},{"key":"26_CR10","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. Adv. Neural. Inf. Process. Syst. 33, 5812\u20135823 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"26_CR11","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116\u20134126. PMLR (2020)"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Jin, M., Zheng, Y., Li, Y.F., Gong, C., Zhou, C., Pan, S. Multi-scale contrastive Siamese networks for self-supervised graph representation learning. arXiv preprint arXiv:2105.05682 (2021)","DOI":"10.24963\/ijcai.2021\/204"},{"key":"26_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2016)"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1735\u20131742 (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Mueller, J., Thyagarajan, A., Doshi-Velez, F.: Siamese recurrent architectures for learning sentence similarity. In: Thirty-First AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10350"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"26_CR17","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems 33, pp. 21271-21284 (2020)"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, H., Yang, X., Wang, Z., Yang, E., Deng, C.: Graph debiased contrastive learning with joint representation clustering. In: IJCAI, pp. 3434\u20133440 (2021)","DOI":"10.24963\/ijcai.2021\/473"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Peng, Z., Liu, H., Jia, Y., Hou, J.: Attention-driven graph clustering network. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 935\u2013943 (2021)","DOI":"10.1145\/3474085.3475276"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Peng, Z., et al.: Graph representation learning via graphical mutual information maximization. In: Proceedings of The Web Conference 2020, pp. 259\u2013270 (2020)","DOI":"10.1145\/3366423.3380112"},{"key":"26_CR21","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)"},{"key":"26_CR22","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., G\u00fcnnemann, S.: Pitfalls of graph neural network evaluation. Computing Research Repository, abs\/1811.05868 (2018)"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, pp. 195\u2013200 (2005)","DOI":"10.1145\/1099554.1099591"},{"issue":"3","key":"26_CR24","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MNET.2006.1637928","volume":"20","author":"O Younis","year":"2006","unstructured":"Younis, O., Krunz, M., Ramasubramanian, S.: Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Network 20(3), 20\u201325 (2006)","journal-title":"IEEE Network"},{"key":"26_CR25","unstructured":"Rend\u00f3n, E., Abundez, I., Arizmendi, A., Quiroz, E.M.: Internal versus external cluster validation indexes. Int. J. Comput. Commun. 5(1), 27-34 (2011)"}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-40283-8_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T22:12:36Z","timestamp":1729894356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-40283-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031402821","9783031402838"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-40283-8_26","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":"9 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"16 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ksem2023.conferences.academy\/","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":"395","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":"114","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":"30","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":"29% - 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":"2,5","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":"5","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)"}}]}}