{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T08:12:38Z","timestamp":1778227958864,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819203659","type":"print"},{"value":"9789819203666","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-92-0366-6_11","type":"book-chapter","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:39:29Z","timestamp":1778225969000},"page":"165-181","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Homophilic and\u00a0Heterophilic-Aware Multi-view Graph Clustering"],"prefix":"10.1007","author":[{"given":"Yeqin","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lirong","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., OConnor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: IJCNN (2020)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)","DOI":"10.1145\/1150402.1150412"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Chao, G., Jiang, Y., Chu, D.: Incomplete contrastive multi-view clustering with high-confidence guiding. In: AAAI (2024)","DOI":"10.1609\/aaai.v38i10.29000"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Chen, B., et al.: Structural deep multi-view clustering with integrated abstraction and detail. Neural Netw. (2024)","DOI":"10.1016\/j.neunet.2024.106287"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Variational graph generator for multiview graph clustering. IEEE TNNLS (2025)","DOI":"10.1109\/TNNLS.2024.3524205"},{"key":"11_CR6","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: ICML. PMLR (2020)"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, J., Wang, Q., Tao, Z., Xie, D., Gao, Q.: Multi-view attribute graph convolution networks for clustering. In: IJCAI (2021)","DOI":"10.24963\/ijcai.2020\/411"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Dong, Z., et al.: Enhanced then progressive fusion with view graph for multi-view clustering. In: CVPR (2025)","DOI":"10.1109\/CVPR52734.2025.01446"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Fan, S., Wang, X., Shi, C., Lu, E., Lin, K., Wang, B.: One2Multi graph autoencoder for multi-view graph clustering. In: The Web Conference (2020)","DOI":"10.1145\/3366423.3380079"},{"key":"11_CR10","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: ICML (2020)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Z., Liu, X., Cen, Y., Dong, Y., Yang, H., Wang, C., Tang, J.: GraphMAE: self-supervised masked graph autoencoders. In: KDD (2022)","DOI":"10.1145\/3534678.3539321"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Z., Ren, Y., Pu, X., He, L.: Non-linear fusion for self-paced multi-view clustering. In: ACM MM (2021)","DOI":"10.1145\/3474085.3475471"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Jia, X., Jing, X.Y., Zhu, X., Cai, Z., Hu, C.H.: Co-embedding: a semi-supervised multi-view representation learning approach. Neural Comput. Appl. (2022)","DOI":"10.1007\/s00521-021-06599-y"},{"key":"11_CR14","unstructured":"Ke, J., et al.: Integrating vision-language semantic graphs in multi-view clustering. In: IJCAI (2024)"},{"key":"11_CR15","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"11_CR16","unstructured":"Krogan, N.J., et\u00a0al.: Global landscape of protein complexes in the yeast saccharomyces cerevisiae. Nature (2006)"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Lin, Z., Kang, Z.: Graph filter-based multi-view attributed graph clustering. In: IJCAI (2021)","DOI":"10.24963\/ijcai.2021\/375"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Ling, Y., et al.: Dual label-guided graph refinement for multi-view graph clustering. In: AAAI (2023)","DOI":"10.1609\/aaai.v37i7.26057"},{"key":"11_CR19","unstructured":"Liu, M., et al.: Deep temporal graph clustering. In: ICLR (2024)"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Reinforcement graph clustering with unknown cluster number. In: ACM MM (2023)","DOI":"10.1145\/3581783.3612155"},{"key":"11_CR21","unstructured":"Pan, E., Kang, Z.: Multi-view contrastive graph clustering. In: NeurIPS (2021)"},{"key":"11_CR22","unstructured":"Pei, H., Wei, B., Chang, K.C.C., Lei, Y., Yang, B.: Geom-GCN: geometric graph convolutional networks. arXiv preprint arXiv:2002.05287 (2020)"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Peng, Y., Zhu, X., Nie, F., Kong, W., Ge, Y.: Fuzzy graph clustering. Inf. Sci. (2021)","DOI":"10.1016\/j.ins.2021.04.058"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Akoglu, L., Iglesias\u00a0Sanchez, P., Muller, E.: Focused clustering and outlier detection in large attributed graphs. In: KDD (2014)","DOI":"10.1145\/2623330.2623682"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"Ren, Y., et al.: A novel federated multi-view clustering method for unaligned and incomplete data fusion. Inf. Fus. (2024)","DOI":"10.1016\/j.inffus.2024.102357"},{"key":"11_CR26","unstructured":"Ren, Y., et al.: Multi-view graph clustering via node-guided contrastive encoding. In: ICML (2025)"},{"key":"11_CR27","unstructured":"Ren, Y., et al.: Dynamic weighted graph fusion for deep multi-view clustering. In: IJCAI (2024)"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Rozemberczki, B., Allen, C., Sarkar, R.: Multi-scale attributed node embedding. J. Complex Netw. (2021)","DOI":"10.1093\/comnet\/cnab014"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Shen, Z., He, H., Kang, Z.: Balanced multi-relational graph clustering. In: ACM MM (2024)","DOI":"10.1145\/3664647.3681325"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML (2008)","DOI":"10.1145\/1390156.1390294"},{"key":"11_CR31","unstructured":"Wang, Y., Chang, D., Fu, Z., Zhao, Y.: Consistent multiple graph embedding for multi-view clustering. IEEE TMM (2021)"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Wu, F., et al.: Semi-supervised multi-view individual and sharable feature learning for webpage classification. In: The Web Conference (2019)","DOI":"10.1145\/3308558.3313492"},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Xia, W., Wang, S., Yang, M., Gao, Q., Han, J., Gao, X.: Multi-view graph embedding clustering network: joint self-supervision and block diagonal representation. Neural Netw. (2022)","DOI":"10.1016\/j.neunet.2021.10.006"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Xu, J., et al.: Deep incomplete multi-view clustering via mining cluster complementarity. In: AAAI (2022)","DOI":"10.1609\/aaai.v36i8.20856"}],"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\/978-981-92-0366-6_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:39:44Z","timestamp":1778225984000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0366-6_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203659","9789819203666"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0366-6_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"9 May 2026","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":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2026.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}