{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:45:44Z","timestamp":1771267544365,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":74,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China &#x28;NSFC&#x29;","award":["62206314"],"award-info":[{"award-number":["62206314"]}]},{"name":"Science and Technology Projects in Guangzhou","award":["2024A04J4388"],"award-info":[{"award-number":["2024A04J4388"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,2,22]]},"DOI":"10.1145\/3773966.3777971","type":"proceedings-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:50:01Z","timestamp":1771264201000},"page":"541-551","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PR-CapsNet: Pseudo-Riemannian Capsule Network with Adaptive Curvature Routing for Graph Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7098-3041","authenticated-orcid":false,"given":"Ye","family":"Qin","sequence":"first","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-539X","authenticated-orcid":false,"given":"Jingchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3928-7495","authenticated-orcid":false,"given":"Yang","family":"Shi","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3303-3111","authenticated-orcid":false,"given":"Haiying","family":"Huang","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2846-9931","authenticated-orcid":false,"given":"Junxu","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5470-9878","authenticated-orcid":false,"given":"Weijian","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9849-9338","authenticated-orcid":false,"given":"Tinghui","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0663-199X","authenticated-orcid":false,"given":"Jinghui","family":"Qin","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016."},{"key":"e_1_3_2_1_2_1","volume-title":"6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. Graph attention networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018."},{"key":"e_1_3_2_1_3_1","first-page":"6861","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri H Souza Jr, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q Weinberger. Simplifying graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 6861-6871. PMLR, 2019."},{"key":"e_1_3_2_1_4_1","volume-title":"2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. In Yoshua Bengio and Yann LeCun, editors, 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, 2014."},{"key":"e_1_3_2_1_5_1","first-page":"14501","article-title":"Recipe for a general, powerful, scalable graph transformer","volume":"35","author":"Ramp\u00e1\u0161ek Ladislav","year":"2022","unstructured":"Ladislav Ramp\u00e1\u0161ek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. Recipe for a general, powerful, scalable graph transformer. Advances in Neural Information Processing Systems, 35:14501-14515, 2022.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_6_1","first-page":"1","volume-title":"Nature Reviews Physics","author":"Bogu\u00f1\u00e1 Mari\u00e1n","year":"2021","unstructured":"Mari\u00e1n Bogu\u00f1\u00e1, Ivan Bonamassa, Manlio De Domenico, Shlomo Havlin, Dmitri Krioukov, and M \u00c1ngeles Serrano. Network geometry. Nature Reviews Physics, pages 1-22, 2021."},{"key":"e_1_3_2_1_7_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Gu Albert","year":"2019","unstructured":"Albert Gu, Frederic Sala, Beliz Gunel, and Christopher R\u00e9. Learning mixed-curvature representations in product spaces. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, 2019."},{"key":"e_1_3_2_1_8_1","volume-title":"Dynamic routing between capsules. Advances in neural information processing systems, 30","author":"Sabour Sara","year":"2017","unstructured":"Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton. Dynamic routing between capsules. Advances in neural information processing systems, 30, 2017."},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on learning representations","author":"Xinyi Zhang","year":"2018","unstructured":"Zhang Xinyi and Lihui Chen. Capsule graph neural network. In International conference on learning representations, 2018."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.06.018"},{"key":"e_1_3_2_1_11_1","volume-title":"Fully hyperbolic neural networks. arXiv preprint arXiv:2105.14686","author":"Chen Weize","year":"2021","unstructured":"Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. Fully hyperbolic neural networks. arXiv preprint arXiv:2105.14686, 2021."},{"key":"e_1_3_2_1_12_1","volume-title":"Motion of a deformable capsule through a hyperbolic constriction. Journal of fluid mechanics, 279:135-163","author":"Leyrat-Maurin Anne","year":"1994","unstructured":"Anne Leyrat-Maurin and Dominique Barthes-Biesel. Motion of a deformable capsule through a hyperbolic constriction. Journal of fluid mechanics, 279:135-163, 1994."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.036106"},{"key":"e_1_3_2_1_14_1","volume-title":"Poincar\u00e9 embeddings for learning hierarchical representations. Advances in neural information processing systems, 30","author":"Nickel Maximillian","year":"2017","unstructured":"Maximillian Nickel and Douwe Kiela. Poincar\u00e9 embeddings for learning hierarchical representations. Advances in neural information processing systems, 30, 2017."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2014.2316836"},{"key":"e_1_3_2_1_16_1","first-page":"8206","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Meng Yu","year":"2019","unstructured":"Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance M Kaplan, and Jiawei Han. Spherical text embedding. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 8206-8215, 2019."},{"key":"e_1_3_2_1_17_1","volume-title":"ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds","author":"Defferrard Micha\u00ebl","year":"2019","unstructured":"Micha\u00ebl Defferrard, Nathana\u00ebl Perraudin, Tomasz Kacprzak, and Raphael Sgier. Deepsphere: towards an equivariant graph-based spherical cnn. In ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds, 2019."},{"key":"e_1_3_2_1_18_1","first-page":"856","volume-title":"Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018","author":"Davidson Tim R","year":"2018","unstructured":"Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. Hyperspherical variational auto-encoders. In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018, pages 856-865. AUAI Press, 2018."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/1005332.1016785"},{"key":"e_1_3_2_1_20_1","volume-title":"Semi-riemannian geometry with applications to relativity","author":"O'neill Barrett","year":"1983","unstructured":"Barrett O'neill. Semi-riemannian geometry with applications to relativity, volume 103. Academic pres, 1983."},{"key":"e_1_3_2_1_21_1","volume-title":"Ultrahyperbolic representation learning. Advances in neural information processing systems, 33:1668-1678","author":"Law Marc","year":"2020","unstructured":"Marc Law and Jos Stam. Ultrahyperbolic representation learning. Advances in neural information processing systems, 33:1668-1678, 2020."},{"key":"e_1_3_2_1_22_1","first-page":"3488","article-title":"Pseudo-riemannian graph convolutional networks","volume":"35","author":"Xiong Bo","year":"2022","unstructured":"Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, and Steffen Staab. Pseudo-riemannian graph convolutional networks. Advances in Neural Information Processing Systems, 35:3488-3501, 2022.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","first-page":"3104482","volume-title":"Icml","volume":"11","author":"Nickel Maximilian","year":"2011","unstructured":"Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel, et al. A three-way model for collective learning on multi-relational data. In Icml, volume 11, pages 3104482-3104584, 2011."},{"key":"e_1_3_2_1_24_1","volume-title":"Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. Advances in neural information processing systems, 26, 2013."},{"key":"e_1_3_2_1_25_1","first-page":"4460","volume-title":"International conference on machine learning","author":"Sala Frederic","year":"2018","unstructured":"Frederic Sala, Chris De Sa, Albert Gu, and Christopher R\u00e9. Representation tradeoffs for hyperbolic embeddings. In International conference on machine learning, pages 4460-4469. PMLR, 2018."},{"key":"e_1_3_2_1_26_1","volume-title":"Metric structures for Riemannian and non-Riemannian spaces","author":"Gromov Mikhael","year":"1999","unstructured":"Mikhael Gromov, Misha Katz, Pierre Pansu, and Stephen Semmes. Metric structures for Riemannian and non-Riemannian spaces, volume 152. Springer, 1999."},{"key":"e_1_3_2_1_27_1","volume-title":"Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359","author":"Chamberlain Benjamin Paul","year":"2017","unstructured":"Benjamin Paul Chamberlain, James Clough, and Marc Peter Deisenroth. Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359, 2017."},{"key":"e_1_3_2_1_28_1","volume-title":"Hyperbolic neural networks. Advances in neural information processing systems, 31","author":"Ganea Octavian","year":"2018","unstructured":"Octavian Ganea, Gary B\u00e9cigneul, and Thomas Hofmann. Hyperbolic neural networks. Advances in neural information processing systems, 31, 2018."},{"key":"e_1_3_2_1_29_1","volume-title":"Hyperbolic graph convolutional neural networks. Advances in neural information processing systems, 32","author":"Chami Ines","year":"2019","unstructured":"Ines Chami, Zhitao Ying, Christopher R\u00e9, and Jure Leskovec. Hyperbolic graph convolutional neural networks. Advances in neural information processing systems, 32, 2019."},{"key":"e_1_3_2_1_30_1","volume-title":"Hyperbolic graph neural networks. Advances in neural information processing systems, 32","author":"Liu Qi","year":"2019","unstructured":"Qi Liu, Maximilian Nickel, and Douwe Kiela. Hyperbolic graph neural networks. Advances in neural information processing systems, 32, 2019."},{"key":"e_1_3_2_1_31_1","first-page":"7548","article-title":"Graph geometry interaction learning","volume":"33","author":"Zhu Shichao","year":"2020","unstructured":"Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, and Bin Wang. Graph geometry interaction learning. Advances in Neural Information Processing Systems, 33:7548-7558, 2020.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449872"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00022"},{"key":"e_1_3_2_1_34_1","first-page":"9681","volume-title":"International Conference on Machine Learning","author":"Sim Aaron","year":"2021","unstructured":"Aaron Sim, Maciej L Wiatrak, Angus Brayne, P\u00e1id\u00ed Creed, and Saee Paliwal. Directed graph embeddings in pseudo-riemannian manifolds. In International Conference on Machine Learning, pages 9681-9690. PMLR, 2021."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","first-page":"2130","DOI":"10.1145\/3534678.3539333","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Xiong Bo","year":"2022","unstructured":"Bo Xiong, Shichao Zhu, Mojtaba Nayyeri, Chengjin Xu, Shirui Pan, Chuan Zhou, and Steffen Staab. Ultrahyperbolic knowledge graph embeddings. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2130-2139, 2022."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_37_1","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1555942"},{"key":"e_1_3_2_1_40_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1):61-80, 2008."},{"key":"e_1_3_2_1_41_1","volume-title":"Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton, Rex Ying, and Jure Leskovec. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584, 2017."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2010350"},{"key":"e_1_3_2_1_44_1","first-page":"2014","volume-title":"International conference on machine learning","author":"Niepert Mathias","year":"2016","unstructured":"Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. Learning convolutional neural networks for graphs. In International conference on machine learning, pages 2014-2023. PMLR, 2016."},{"key":"e_1_3_2_1_45_1","first-page":"1469","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Gu Jindong","year":"2021","unstructured":"Jindong Gu. Interpretable graph capsule networks for object recognition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1469-1477, 2021."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00164"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331216"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413715"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1016\/j.patrec.2020.09.010","article-title":"A capsule network-based framework for identification of covid-19 cases from x-ray images","volume":"138","author":"Afshar Parnian","year":"2020","unstructured":"Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos N Plataniotis, and Arash Mohammadi. Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recognition Letters, 138:638-643, 2020.","journal-title":"Pattern Recognition Letters"},{"key":"e_1_3_2_1_50_1","first-page":"1368","volume-title":"ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP)","author":"Afshar Parnian","year":"2019","unstructured":"Parnian Afshar, Konstantinos N Plataniotis, and Arash Mohammadi. Capsule networks for brain tumor classification based on mri images and coarse tumor boundaries. In ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 1368-1372. IEEE, 2019."},{"issue":"5","key":"e_1_3_2_1_51_1","first-page":"1208","article-title":"multi-head attention routing-based capsule network for covid-19 chest x-ray image classification","volume":"41","author":"Li Fudong","year":"2021","unstructured":"Fudong Li, Xingyu Lu, and Jianjun Yuan. Mha-corocapsule: multi-head attention routing-based capsule network for covid-19 chest x-ray image classification. IEEE Transactions on Medical Imaging, 41(5):1208-1218, 2021.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"e_1_3_2_1_52_1","volume-title":"Star-caps: Capsule networks with straight-through attentive routing. Advances in neural information processing systems, 32","author":"Ahmed Karim","year":"2019","unstructured":"Karim Ahmed and Lorenzo Torresani. Star-caps: Capsule networks with straight-through attentive routing. Advances in neural information processing systems, 32, 2019."},{"key":"e_1_3_2_1_53_1","volume-title":"Introducing routing uncertainty in capsule networks. Advances in neural information processing systems, 33:6490-6502","author":"Sousa Ribeiro Fabio De","year":"2020","unstructured":"Fabio De Sousa Ribeiro, Georgios Leontidis, and Stefanos Kollias. Introducing routing uncertainty in capsule networks. Advances in neural information processing systems, 33:6490-6502, 2020."},{"key":"e_1_3_2_1_54_1","volume-title":"Protocaps: A fast and non-iterative capsule network routing method. arXiv preprint arXiv:2307.09944","author":"Everett Miles","year":"2023","unstructured":"Miles Everett, Mingjun Zhong, and Georgios Leontidis. Protocaps: A fast and non-iterative capsule network routing method. arXiv preprint arXiv:2307.09944, 2023."},{"key":"e_1_3_2_1_55_1","volume-title":"Investigating capsule networks with dynamic routing for text classification. arXiv preprint arXiv:1804.00538","author":"Zhao Wei","year":"2018","unstructured":"Wei Zhao, Jianbo Ye, Min Yang, Zeyang Lei, Suofei Zhang, and Zhou Zhao. Investigating capsule networks with dynamic routing for text classification. arXiv preprint arXiv:1804.00538, 2018."},{"key":"e_1_3_2_1_56_1","first-page":"456","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Du Chunning","year":"2019","unstructured":"Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, Jianxin Liao, Chun Wang, and Bing Ma. Investigating capsule network and semantic feature on hyperplanes for text classification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 456-465, 2019."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/276675.276685"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_1_60_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868, 2018."},{"key":"e_1_3_2_1_61_1","volume-title":"Subgraph matching kernels for attributed graphs. arXiv preprint arXiv:1206.6483","author":"Kriege Nils","year":"2012","unstructured":"Nils Kriege and Petra Mutzel. Subgraph matching kernels for attributed graphs. arXiv preprint arXiv:1206.6483, 2012."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"e_1_3_2_1_63_1","volume-title":"Scalable kernels for graphs with continuous attributes. Advances in neural information processing systems, 26","author":"Feragen Aasa","year":"2013","unstructured":"Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne, and Karsten Borgwardt. Scalable kernels for graphs with continuous attributes. Advances in neural information processing systems, 26, 2013."},{"key":"e_1_3_2_1_64_1","volume-title":"Stefan Sch\u00f6nauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(suppl_1):i47-i56","author":"Borgwardt Karsten M","year":"2005","unstructured":"Karsten M Borgwardt, Cheng Soon Ong, Stefan Sch\u00f6nauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(suppl_1):i47-i56, 2005."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"},{"issue":"16","key":"e_1_3_2_1_66_1","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1093\/bioinformatics\/btt343","article-title":"a simulation framework for testing the association of genomic intervals","volume":"29","author":"Heger Andreas","year":"2013","unstructured":"Andreas Heger, Caleb Webber, Martin Goodson, Chris P Ponting, and Gerton Lunter. Gat: a simulation framework for testing the association of genomic intervals. Bioinformatics, 29(16):2046-2048, 2013.","journal-title":"Bioinformatics"},{"key":"e_1_3_2_1_67_1","volume-title":"How attentive are graph attention networks? arXiv preprint arXiv:2105.14491","author":"Brody Shaked","year":"2021","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? arXiv preprint arXiv:2105.14491, 2021."},{"key":"e_1_3_2_1_68_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018."},{"key":"e_1_3_2_1_69_1","volume-title":"International conference on learning representations","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz. Simple spectral graph convolution. In International conference on learning representations, 2021."},{"key":"e_1_3_2_1_70_1","first-page":"3488","article-title":"Pseudo-riemannian graph convolutional networks","volume":"35","author":"Xiong Bo","year":"2022","unstructured":"Bo Xiong, Shichao Zhu, Nico Potyka, Shirui Pan, Chuan Zhou, and Steffen Staab. Pseudo-riemannian graph convolutional networks. Advances in Neural Information Processing Systems, 35:3488-3501, 2022.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_71_1","first-page":"97650","article-title":"Classic gnns are strong baselines: Reassessing gnns for node classification","volume":"37","author":"Luo Yuankai","year":"2024","unstructured":"Yuankai Luo, Lei Shi, and Xiao-Ming Wu. Classic gnns are strong baselines: Reassessing gnns for node classification. Advances in Neural Information Processing Systems, 37:97650-97669, 2024.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25586"},{"key":"e_1_3_2_1_73_1","first-page":"137698","article-title":"Graph classification via reference distribution learning: theory and practice","volume":"37","author":"Wang Zixiao","year":"2024","unstructured":"Zixiao Wang and Jicong Fan. Graph classification via reference distribution learning: theory and practice. Advances in Neural Information Processing Systems, 37:137698-137740, 2024.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_74_1","volume-title":"Forty-second International Conference on Machine Learning.","author":"Abbahaddou Yassine","unstructured":"Yassine Abbahaddou, Fragkiskos D Malliaros, Johannes F Lutzeyer, Amine M Aboussalah, and Michalis Vazirgiannis. Graph neural network generalization with gaussian mixture model based augmentation. In Forty-second International Conference on Machine Learning."}],"event":{"name":"WSDM '26:The Nineteenth ACM International Conference on Web Search and Data Mining","location":"Boise ID USA","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:59:36Z","timestamp":1771264776000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3773966.3777971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":74,"alternative-id":["10.1145\/3773966.3777971","10.1145\/3773966"],"URL":"https:\/\/doi.org\/10.1145\/3773966.3777971","relation":{},"subject":[],"published":{"date-parts":[[2026,2,21]]},"assertion":[{"value":"2026-02-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}