Abstract
Hypergraph-based methods can learn non-pairwise associations more efficiently in many real-world datasets. However, existing hypergraph-based methods do not consider the relationship of the hybrid neighborhood. To address this issue, we propose a hybrid higher-order neighborhood based hypergraph convolutional network (HybridHGCN). Technically, feature embeddings are generated via k-hop hypergraph convolution layers and mixed by the hybrid message operator. To evaluate the proposed HybridHGCN, we conduct experiments on the citation network datasets and the visual object datasets. The experimental results show that HybridHGCN brings significant improvements over state-of-the-art hypergraph neural network baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abu-El-Haija, S., et al.: Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing. In: Proceedings of the International Conference on Machine Learning, pp. 21–29 (2019)
Agarwal, S., Branson, K., Belongie, S.: Higher order learning with graphs. In: Proceedings of the International Conference on Machine Learning, pp. 17–24 (2006)
Bai, S., Zhang, F., Torr, P.H.S.: Hypergraph convolution and hypergraph attention. Pattern Recogn. 110, 107637 (2021)
Bandyopadhyay, S., Das, K., Narasimha Murty, M.: Line hypergraph convolution network: Applying graph convolution for hypergraphs (2020). arXiv preprint: arXiv:2002.03392
Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166(2016)
Bretto, A.: Hypergraph Theory. An Introduction. Mathematical Engineering. Springer, Cham (2013)
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)
Chen, D., Tian, X., Shen, Y., Ouhyoung, M.: On visual similarity based 3d model retrieval. Comput. Graph. Forum 22, 223–232 (2003)
Chen, F., Wang, Y., Wang, B., Jay Kuo, C.-C.: Graph representation learning: a survey. APSIPA Trans. Signal Inf. Process. 9 (2020)
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. 3558–3565 (2019)
Feng, Y., Zhang, Z., Zhao, X., Ji, R., Gao, Y.: GVCNN: group-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–272 (2018)
Hayashi, K., Aksoy, S.G., Park, C.H., Park, H.: Hypergraph random walks, laplacians, and clustering. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 495–504 (2020)
Jiang, J., Wei, Y., Feng, Y., Cao, J., Gao, Y.: Dynamic hypergraph neural networks. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2635–2641 (2019)
Kim, E.-S., Kang, W.Y., On, K.-W., Heo, Y.-J., Zhang, B.-T.: Hypergraph attention networks for multimodal learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14581–14590 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016). arXiv preprint: arXiv:1609.02907
Li, P., Faltings, B.: Hypergraph learning with hyperedge expansion. In: Flach, P.A., De Bie, T., Cristianini, N., (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. LNCS, vol. 7523, pp. 410–425. Springer, Berlin (2021). https://doi.org/10.1007/978-3-642-33460-3_32
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)
Shchur, O., Mumme, M., Bojchevski, A., Günnemann, S.: Pitfalls of graph neural network evaluation (2018). arXiv preprint: arXiv:1811.05868
Shi, H., et al.: Hypergraph-induced convolutional networks for visual classification. IEEE Trans. Neural Netw. Learn. Syst. 30(10), 2963–2972 (2018)
Hang, S., Subhransu, M., Evangelos, K., Learned-Miller, E.: Multi-view convolutional neural networks for 3d shape recognition. In: Proceedings of the IEEE International Conference On Computer Vision, pp. 945–953 (2015)
Su, L., Gao, Y., Zhao, X., Wan, H., Gu, M., Sun, J.: Vertex-weighted hypergraph learning for multi-view object classification. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2779–2785 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks (2017). arXiv preprint: arXiv:1710.10903
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2020)
Wu, Z., et al.: 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, pp. 1912–1920 (2015)
Yadati, N., et al.: HyperGCN: a new method of training graph convolutional networks on hypergraphs (2018). arXiv preprint: arXiv:1809.02589
Jun Yu, Dacheng Tao, and Meng Wang.: Adaptive hypergraph learning and its application in image classification. IEEE Transactions on Image Processing, vol. 21, no. 7, pp. 3262–3272(2012)
Zhang, Z., Lin, H., Gao, Y.: KLISS BNRist.: dynamic hypergraph structure learning. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 3162–3169 (2018)
Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. Adv. Neural Inf. Process. Syst. 19, 1601–1608 (2006)
Zhou, J., et al.: Graph neural networks: a review of methods and applications (2018). arXiv preprint: arXiv:1812.08434
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant U1701266, in part by Guangdong Provincial Key Laboratory of Intellectual Property and Big Data under Grant 2018B030322016, in part by Special Projects for Key Fields in Higher Education of Guangdong under Grant 2020ZDZX3077 and Grant 2021ZDZX1042, and in part by Qingyuan Science and Technology Plan Project under Grant 170809111721249 and Grant 170802171710591.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, J., Lei, F., Wang, S., Wang, S., Dai, Q. (2021). Hypergraph Convolutional Network with Hybrid Higher-Order Neighbors. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-88013-2_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88012-5
Online ISBN: 978-3-030-88013-2
eBook Packages: Computer ScienceComputer Science (R0)