{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:00:53Z","timestamp":1743040853878,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819755714"},{"type":"electronic","value":"9789819755721"}],"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\/978-981-97-5572-1_27","type":"book-chapter","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T23:03:11Z","timestamp":1725058991000},"page":"385-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Latent Representation Ranking for\u00a0Masked Graph Autoencoder"],"prefix":"10.1007","author":[{"given":"Yulan","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sheng","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhirui","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junchen","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuzheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangquan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"key":"27_CR1","unstructured":"Guo, Z., et al.: Linkless link prediction via relational distillation (2023)"},{"key":"27_CR2","unstructured":"Hasanzadeh, A., Hajiramezanali, E., Narayanan, K., Duffield, N., Zhou, M., Qian, X.: Semi-implicit graph variational auto-encoders. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"27_CR3","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":"27_CR4","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Hou, Z., et al.: GraphMAE2: a decoding-enhanced masked self-supervised graph learner (2023)","DOI":"10.1145\/3543507.3583379"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Hou, Z., et al.: GraphMAE: self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)","DOI":"10.1145\/3534678.3539321"},{"key":"27_CR7","unstructured":"Hu, Y., et al.: Do we really need contrastive learning for graph representation? (2023)"},{"key":"27_CR8","unstructured":"Hu, Y., Ouyang, S., Yang, Z., Liu, Y.: VIGraph: self-supervised learning for class-imbalanced node classification. arXiv preprint arXiv:2311.01191 (2023)"},{"key":"27_CR9","unstructured":"Hu, Y., Yang, Z., Ouyang, S., Liu, Y.: HGCVAE: integrating generative and contrastive learning for heterogeneous graph learning. arXiv preprint arXiv:2310.11102 (2023)"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., Chang, K.W., Sun, Y.: GPT-GNN: generative pre-training of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1857\u20131867 (2020)","DOI":"10.1145\/3394486.3403237"},{"key":"27_CR11","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"27_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"27_CR13","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Li, X., Ye, T., Shan, C., Li, D., Gao, M.: SeeGera: self-supervised semi-implicit graph variational auto-encoders with masking. In: Proceedings of the ACM Web Conference 2023, pp. 143\u2013153 (2023)","DOI":"10.1145\/3543507.3583245"},{"key":"27_CR15","unstructured":"Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)"},{"key":"27_CR16","unstructured":"Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding (2019)","DOI":"10.24963\/ijcai.2018\/362"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Qiu, J., et al.: GCC: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1150\u20131160 (2020)","DOI":"10.1145\/3394486.3403168"},{"key":"27_CR19","unstructured":"Sun, F.Y., Hoffmann, J., Verma, V., Tang, J.: InfoGraph: unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000 (2019)"},{"key":"27_CR20","doi-asserted-by":"crossref","unstructured":"Tan, Q., et al.: S2GAE: self-supervised graph autoencoders are generalizable learners with graph masking. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 787\u2013795 (2023)","DOI":"10.1145\/3539597.3570404"},{"key":"27_CR21","unstructured":"Thakoor, S., et al.: Large-scale representation learning on graphs via bootstrapping. arXiv preprint arXiv:2102.06514 (2021)"},{"issue":"20","key":"27_CR22","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Statistics 1050(20), 10\u201348550 (2017)","journal-title":"Statistics"},{"key":"27_CR23","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)"},{"key":"27_CR24","first-page":"30414","volume":"34","author":"D Xu","year":"2021","unstructured":"Xu, D., Cheng, W., Luo, D., Chen, H., Zhang, X.: InfoGCL: information-aware graph contrastive learning. Adv. Neural. Inf. Process. Syst. 34, 30414\u201330425 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR25","unstructured":"You, Y., Chen, T., Shen, Y., Wang, Z.: Graph contrastive learning automated. In: International Conference on Machine Learning, pp. 12121\u201312132. PMLR (2021)"},{"key":"27_CR26","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":"27_CR27","first-page":"76","volume":"34","author":"H Zhang","year":"2021","unstructured":"Zhang, H., Wu, Q., Yan, J., Wipf, D., Yu, P.S.: From canonical correlation analysis to self-supervised graph neural networks. Adv. Neural. Inf. Process. Syst. 34, 76\u201389 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR28","unstructured":"Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5572-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T23:07:55Z","timestamp":1725059275000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5572-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755714","9789819755721"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5572-1_27","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":"31 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}