{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T12:21:13Z","timestamp":1773490873332,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819570744","type":"print"},{"value":"9789819570751","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\/978-981-95-7075-1_23","type":"book-chapter","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:13:47Z","timestamp":1773486827000},"page":"346-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Topology-Guided Hypergraph Transformer Network: Unveiling Structural Insights"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3110-8843","authenticated-orcid":false,"given":"Khaled Mohammed","family":"Saifuddin","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9527-9600","authenticated-orcid":false,"given":"Mehmet Emin","family":"Aktas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8817-2442","authenticated-orcid":false,"given":"Esra","family":"Akbas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,15]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637","volume":"110","author":"S Bai","year":"2021","unstructured":"Bai, S., Zhang, F., Torr, P.H.: Hypergraph convolution and hypergraph attention. Pattern Recogn. 110, 107637 (2021)","journal-title":"Pattern Recogn."},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Cai, D., Song, M., Sun, C., Zhang, B., Hong, S., Li, H.: Hypergraph structure learning for hypergraph neural networks. In: De Raedt, L. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, vol. 7, pp. 1923\u20131929 (2022)","DOI":"10.24963\/ijcai.2022\/267"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chen, D., Shang, M., Lv, Z., Fu, Y.: Detecting overlapping communities of weighted networks via a local algorithm. Physica A 389(19), 4177\u20134187 (2010)","DOI":"10.1016\/j.physa.2010.05.046"},{"key":"23_CR4","unstructured":"Chen, J., Gao, K., Li, G., He, K.: NAGphormer: a tokenized graph transformer for node classification in large graphs. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"23_CR5","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: Simple and deep graph convolutional networks. In: International Conference on Machine Learning, pp. 1725\u20131735. PMLR (2020)"},{"key":"23_CR6","unstructured":"Chien, E., Pan, C., Peng, J., Milenkovic, O.: You are ALLSet: a multiset function framework for hypergraph neural networks. arXiv preprint arXiv:2106.13264 (2021)"},{"key":"23_CR7","unstructured":"Chien, E., Peng, J., Milenkovic, O.: Adaptive universal generalized PageRank graph neural network. In: International Conference on Learning Representations (2021)"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Ding, K., et al.: HyperFormer: learning expressive sparse feature representations via hypergraph transformer. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (2023)","DOI":"10.1145\/3539618.3591999"},{"key":"23_CR9","unstructured":"Dong, Y., Sawin, W., Bengio, Y.: HNHN: hypergraph networks with hyperedge neurons. arXiv preprint arXiv:2006.12278 (2020)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Duta, I., Cassar\u00e0, G., Silvestri, F., Li\u00f2, P.: Sheaf hypergraph networks. In: Advances in Neural Information Processing Systems, vol. 36 (2024)","DOI":"10.52202\/075280-0529"},{"key":"23_CR11","unstructured":"Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699 (2020)"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3558\u20133565 (2019)","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Huang, J., Yang, J.: UniGNN: a unified framework for graph and hypergraph neural networks. arXiv preprint arXiv:2105.00956 (2021)","DOI":"10.24963\/ijcai.2021\/353"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wei, Y., Feng, Y., Cao, J., Gao, Y.: Dynamic hypergraph neural networks. In: IJCAI, pp. 2635\u20132641 (2019)","DOI":"10.24963\/ijcai.2019\/366"},{"key":"23_CR15","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ins.2022.10.006","volume":"616","author":"X Kang","year":"2022","unstructured":"Kang, X., et al.: Dynamic hypergraph neural networks based on key hyperedges. Inf. Sci. 616, 37\u201351 (2022)","journal-title":"Inf. Sci."},{"key":"23_CR16","unstructured":"Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., Teh, Y.W.: Set transformer: a framework for attention-based permutation-invariant neural networks. In: International Conference on Machine Learning, pp. 3744\u20133753. PMLR (2019)"},{"issue":"5","key":"23_CR17","first-page":"1","volume":"17","author":"M Li","year":"2023","unstructured":"Li, M., Zhang, Y., Li, X., Zhang, Y., Yin, B.: Hypergraph transformer neural networks. ACM Trans. Knowl. Discov. Data 17(5), 1\u201322 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"23_CR18","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neunet.2022.08.028","volume":"157","author":"J Liu","year":"2023","unstructured":"Liu, J., Song, L., Wang, G., Shang, X.: Meta-HGT: metapath-aware HyperGraph transformer for heterogeneous information network embedding. Neural Netw. 157, 65\u201376 (2023)","journal-title":"Neural Netw."},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Luo, Y.: SHINE: subhypergraph inductive neural network. In: Advances in Neural Information Processing Systems, vol. 35, pp. 18779\u201318792 (2022)","DOI":"10.52202\/068431-1364"},{"key":"23_CR20","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"6","key":"23_CR21","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1016\/j.aml.2008.07.020","volume":"22","author":"J Rodriguez","year":"2009","unstructured":"Rodriguez, J.: Laplacian eigenvalues and partition problems in hypergraphs. Appl. Math. Lett. 22(6), 916\u2013921 (2009)","journal-title":"Appl. Math. Lett."},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Saifuddin, K.M., May, C., Tanvir, F., Islam, M.I.K., Akbas, E.: Seq-HYGAN: sequence classification via hypergraph attention network. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, pp. 2167\u20132177 (2023)","DOI":"10.1145\/3583780.3615057"},{"issue":"3","key":"23_CR23","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/0378-8733(83)90028-X","volume":"5","author":"SB Seidman","year":"1983","unstructured":"Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269\u2013287 (1983)","journal-title":"Soc. Netw."},{"key":"23_CR24","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"23_CR25","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"23_CR26","unstructured":"Wang, M., et al.: Deep graph library: a graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019)"},{"key":"23_CR27","unstructured":"Yadati, N., Nimishakavi, M., Yadav, P., Nitin, V., Louis, A., Talukdar, P.: HyperGCN: a new method for training graph convolutional networks on hypergraphs. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"23_CR28","unstructured":"Ying, C., et al.: Do transformers really perform badly for graph representation? In: Advances in Neural Information Processing Systems, vol. 34, pp. 28877\u201328888 (2021)"},{"key":"23_CR29","unstructured":"Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"23_CR30","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, X., Zhang, J.: HEGEL: hypergraph transformer for long document summarization. arXiv preprint arXiv:2210.04126 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.692"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, Q., Hu, Q., Lee, C.K.: Hierarchical graph transformer with adaptive node sampling. In: Advances in Neural Information Processing Systems, vol. 35, pp. 21171\u201321183 (2022)","DOI":"10.52202\/068431-1539"},{"key":"23_CR33","unstructured":"Zhao, J., et al.: Gophormer: ego-graph transformer for node classification. arXiv preprint arXiv:2110.13094 (2021)"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2025: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-7075-1_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:13:52Z","timestamp":1773486832000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-7075-1_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819570744","9789819570751"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-7075-1_23","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":"15 March 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wellington","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}