{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T08:12:12Z","timestamp":1778227932289,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":49,"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_15","type":"book-chapter","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:37:42Z","timestamp":1778225862000},"page":"233-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HyReaL: Clustering Attributed Graph via\u00a0Hyper-complex Space Representation Learning"],"prefix":"10.1007","author":[{"given":"Junyang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengke","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuie","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiqun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiu-ming","family":"Cheung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., Cui, P.: Structural deep clustering network. In: Proceedings of the 29th Web Conference, April 2020, pp. 1400\u20131410 (2020)","DOI":"10.1145\/3366423.3380214"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 (2015)","DOI":"10.18653\/v1\/K16-1002"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Chen, J., Ji, Y., Zou, R., Zhang, Y., Cheung, Y.: QGRL: quaternion graph representation learning for heterogeneous feature data clustering. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 297\u2013306 (2024)","DOI":"10.1145\/3637528.3671839"},{"key":"15_CR4","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: Proceedings of the 37th International Conference on Machine Learning, vol.\u00a0119, pp. 1725\u20131735 (2020)"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Comminiello, D., Lella, M., Scardapane, S., Uncini, A.: Quaternion convolutional neural networks for detection and localization of 3D sound events. In: Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing, pp. 8533\u20138537 (2019)","DOI":"10.1109\/ICASSP.2019.8682711"},{"key":"15_CR6","unstructured":"Eliasof, M., Haber, E., Treister, E.: PDE-GCN: novel architectures for graph neural networks motivated by partial differential equations. In: Proceedings of Advances in Neural Information Processing Systems, pp. 3836\u20133849 (2021)"},{"key":"15_CR7","unstructured":"Ester, M., Kriegel, H., Xu, X.: A database interface for clustering in large spatial databases. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining, pp. 94\u201399 (1995)"},{"key":"15_CR8","unstructured":"Hamerly, G., Elkan, C.: Learning the k in k-means. In: Proceedings of the 16th International Conference on Neural Information Processing Systems, pp. 281\u2013288 (2003)"},{"issue":"4","key":"15_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3447772","volume":"54","author":"A Hogan","year":"2021","unstructured":"Hogan, A., et al.: Knowledge graphs. ACM Comput. Surv. 54(4), 1\u201337 (2021)","journal-title":"ACM Comput. Surv."},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","volume":"622","author":"AM Ikotun","year":"2023","unstructured":"Ikotun, A.M., Ezugwu, A.E., Abualigah, L., Abuhaija, B., Heming, J.: K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of Big Data. Inf. Sci. 622, 178\u2013210 (2023)","journal-title":"Inf. Sci."},{"key":"15_CR11","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"15_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations (2017)"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1016\/j.ins.2022.06.075","volume":"607","author":"S Kumar","year":"2022","unstructured":"Kumar, S., Mallik, A., Khetarpal, A., Panda, B.S.: Influence maximization in social networks using graph embedding and graph neural network. Inf. Sci. 607, 1617\u20131636 (2022)","journal-title":"Inf. Sci."},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp. 3538\u20133545 (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"15_CR15","unstructured":"Liu, Y., Xia, J., Zhou, S., et al.: A survey of deep graph clustering: taxonomy, challenge, and application. arXiv preprint arXiv:2211.12875 (2022)"},{"key":"15_CR16","unstructured":"Liu, Y.: A survey of deep graph clustering: taxonomy, challenge, and application. arXiv preprint arXiv:2211.12875 (2022)"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Revisiting modularity maximization for graph clustering: a contrastive learning perspective. In: Proceedings of the 30th SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1968\u20131979 (2024)","DOI":"10.1145\/3637528.3671967"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Ma, Y., Zhan, K.: Self-contrastive graph diffusion network. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 3857\u20133865 (2023)","DOI":"10.1145\/3581783.3611815"},{"key":"15_CR19","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C.: Adversarially regularized graph autoencoder for graph embedding. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 2609\u20132615 (2018)","DOI":"10.24963\/ijcai.2018\/362"},{"key":"15_CR21","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.1007\/s10462-019-09752-1","volume":"53","author":"T Parcollet","year":"2020","unstructured":"Parcollet, T., Morchid, M., Linar\u00e8s, G.: A survey of quaternion neural networks. Artif. Intell. Rev. 53, 2957\u20132982 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Ren, Z., Sun, Q., Wei, D.: Multiple kernel clustering with kernel k-means coupled graph tensor learning. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, February 2021, vol.\u00a035, pp. 9411\u20139418 (2011)","DOI":"10.1609\/aaai.v35i11.17134"},{"issue":"6","key":"15_CR23","doi-asserted-by":"publisher","first-page":"3273","DOI":"10.1109\/TCYB.2020.3000947","volume":"51","author":"Z Ren","year":"2020","unstructured":"Ren, Z., Yang, S.X., Sun, Q., Wang, T.: Consensus affinity graph learning for multiple kernel clustering. IEEE Trans. Cybern. 51(6), 3273\u20133284 (2020)","journal-title":"IEEE Trans. Cybern."},{"key":"15_CR24","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: DropEdge: towards deep graph convolutional networks on node classification. In: Proceedings of 8th International Conference on Learning Representations (2020)"},{"key":"15_CR25","unstructured":"Rusch, T.K., Chamberlain, B., Rowbottom, J., Mishra, S., Bronstein, M.M.: Graph-coupled oscillator networks. In: Proceedings of International Conference on Machine Learning, vol.\u00a0162, pp. 18888\u201318909 (2022)"},{"issue":"3","key":"15_CR26","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93\u2013106 (2008)","journal-title":"AI Mag."},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Tu, W., et al.: Deep fusion clustering network. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence, pp. 9978\u20139987 (2021)","DOI":"10.1609\/aaai.v35i11.17198"},{"key":"15_CR28","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Von Luxburg","year":"2007","unstructured":"Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17, 395\u2013416 (2007)","journal-title":"Stat. Comput."},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., Zhang, C.: Attributed graph clustering: a deep attentional embedding approach. In: Kraus, S. (ed.) Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3670\u20133676 (2019)","DOI":"10.24963\/ijcai.2019\/509"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. 55(5), 97:1\u201397:37 (2023)","DOI":"10.1145\/3535101"},{"key":"15_CR31","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: Proceedings of the 35th International Conference on Machine Learning, vol.\u00a080, pp. 5449\u20135458 (2018)"},{"issue":"6","key":"15_CR32","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1109\/TAI.2024.3413694","volume":"6","author":"Y Xu","year":"2025","unstructured":"Xu, Y., Huang, D., Wang, C., Lai, J.: GLAC-GCN: global and local topology-aware contrastive graph clustering network. IEEE Tans. Artif. Intell. 6(6), 1448\u20131459 (2025)","journal-title":"IEEE Tans. Artif. Intell."},{"key":"15_CR33","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 Artificial Intelligence, pp. 2111\u20132117 (2015)"},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Yang, X., et al.: Cluster-guided contrastive graph clustering network. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence, pp. 10834\u201310842 (2023)","DOI":"10.1609\/aaai.v37i9.26285"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Yang, X., et al.: CONVERT: contrastive graph clustering with reliable augmentation. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 319\u2013327 (2023)","DOI":"10.1145\/3581783.3611809"},{"key":"15_CR36","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/0024-3795(95)00543-9","volume":"251","author":"F Zhang","year":"1997","unstructured":"Zhang, F.: Quaternions and matrices of quaternions. Linear Algebra Appl. 251, 21\u201357 (1997)","journal-title":"Linear Algebra Appl."},{"key":"15_CR37","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, P., Zhang, R., Li, X.: Embedding graph auto-encoder for graph clustering. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3158654"},{"key":"15_CR38","unstructured":"Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embeddings. In: Processing of the 32nd Conference on Neural Information Processing Systems, pp. 2731\u20132741 (2019)"},{"key":"15_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y.: Graph-based dissimilarity measurement for cluster analysis of any-type-attributed data. IEEE Trans. Neural Netw. Learn. Syst. 34(9), 6530\u20136544 (2022)","DOI":"10.1109\/TNNLS.2022.3202700"},{"key":"15_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Cheung, Y., Zeng, A.: Het2Hom: representation of heterogeneous attributes into homogeneous concept spaces for categorical-and-numerical-attribute data clustering. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence, pp. 3758\u20133765 (2022)","DOI":"10.24963\/ijcai.2022\/522"},{"issue":"9","key":"15_CR41","doi-asserted-by":"publisher","first-page":"16049","DOI":"10.1109\/TNNLS.2025.3563769","volume":"36","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., et al.: Learning self-growth maps for fast and accurate imbalanced streaming data clustering. IEEE Trans. Neural Netw. Learn. Syst. 36(9), 16049\u201316061 (2025)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"15_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tan, Z., Luo, X., Liu, Y.: Hierarchical reference sets for robust unsupervised detection of scattered and clustered outliers. IEEE IoT J. (2025)","DOI":"10.1109\/JIOT.2025.3644591"},{"key":"15_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhao, M., Chen, Y., Lu, Y., Cheung, Y.: Learning unified distance metric for heterogeneous attribute data clustering. Exp. Syst. Appl. 273, 126738 (2025)","DOI":"10.1016\/j.eswa.2025.126738"},{"issue":"6","key":"15_CR44","first-page":"1","volume":"3","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Zhao, M., Jia, H., Li, M., Lu, Y., Cheung, Y.: Categorical data clustering via value order estimated distance metric learning. Proc. ACM Manage. Data 3(6), 1\u201324 (2025)","journal-title":"Proc. ACM Manage. Data"},{"key":"15_CR45","unstructured":"Zhao, L., Akoglu, L.: PairNorm: tackling oversmoothing in GNNs. In: Proceedings of 8th International Conference on Learning Representations (2020)"},{"key":"15_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, M., Feng, S., Zhang, Y., Li, M., Lu, Y., Cheung, Y.: Learning order forest for qualitative-attribute data clustering. In: Proceedings of the 27th European Conference on Artificial Intelligence, pp. 1943\u20131950 (2024)","DOI":"10.3233\/FAIA240709"},{"key":"15_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, M., et al.: Break the tie: learning cluster-customized category relationships for categorical data clustering. In: Proceedings of the 40th AAAI Conference on Artificial Intelligence (2026)","DOI":"10.1609\/aaai.v40i34.40104"},{"issue":"5","key":"15_CR48","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1109\/TCSVT.2022.3223150","volume":"33","author":"Z Zheng","year":"2023","unstructured":"Zheng, Z., Huang, G., Yuan, X., Pun, C., Liu, H., Ling, W.: Quaternion-valued correlation learning for few-shot semantic segmentation. IEEE Trans. Circ. Syst. Video Technol. 33(5), 2102\u20132115 (2023)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"15_CR49","unstructured":"Zhou, K., et al.: Dirichlet energy constrained learning for deep graph neural networks. In: Proceedings of Advances in Neural Information Processing Systems, pp. 21834\u201321846 (2021)"}],"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_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:38:12Z","timestamp":1778225892000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0366-6_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203659","9789819203666"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0366-6_15","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"}}]}}