research-article

Free access

Hypergraph neural networks

AUTHORs:

Yue GaoAuthors Info & Claims

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

Article No.: 437, Pages 3558 - 3565

https://doi.org/10.1609/aaai.v33i01.33013558

Published: 27 January 2019 Publication History

PDF eReader Publisher Site

Abstract

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.

References

[1]

Atwood, J., and Towsley, D. 2016. Diffusion-Convolutional Neural Networks. In NIPS, 1993-2001.

[2]

Bruna, J.; Zaremba, W.; Szlam, A.; and LeCun, Y. 2014. Spectral Networks and Locally Connected Networks on Graphs. In Proc. ICLR.

[3]

Chen, D.-Y.; Tian, X.-P.; Shen, Y.-T.; and Ouhyoung, M. 2003. On Visual Similarity Based 3D Model Retrieval. In Computer Graphics Forum, volume 22, 223-232. Wiley Online Library.

[4]

Defferrard, M.; Bresson, X.; and Vandergheynst, R 2016. Convolutional Neural Networks on Graphs with Gast Localized Spectral Filtering. In NIPS, 3844-3852.

[5]

Feng, Y.; Zhang, Z.; Zhao, X.; Ji, R.; and Gao, Y. 2018. Gvcnn: Group-View Convolutional Neural Networks for 3D Shape Recognition. In Proc. CVPR, 264-272.

[6]

Gao, Y.; Wang, M.; Tao, D.; Ji, R.; and Dai, Q. 2012. 3-D Object Retrieval and Recognition with Hypergraph Analysis. IEEE Transactions on Image Processing 21(9):4290-4303.

Digital Library

[7]

Gao, Y.; Wang, M.; Zha, Z.-J.; Shen, J.; Li, X.; and Wu, X. 2013. Visual-Textual Joint Relevance Learning for Tagbased Social Image Search. IEEE Transactions on Image Processing 22(1):363-376.

Digital Library

[8]

Gori, M.; Monfardini, G.; and Scarselli, F. 2005. A New Model for Learning in Graph Domains. In Proc. IJCNN, volume 2, 729-734. IEEE.

[9]

Henaff, M.; Bruna, J.; and LeCun, Y. 2015. Deep Convolutional Networks on Graph-Structured Data. arXiv preprint arXiv:I506.05I63.

[10]

Huang, Y.; Liu, Q ; Zhang, S.; and Metaxas, D. N. 2010. Image Retrieval via Probabilistic Hypergraph Ranking. In Proc. CVPR, 3376-3383. IEEE.

[11]

Huang, Y.; Liu, Q.; and Metaxas, D. 2009. Video Object Segmentation by Hypergraph Cut. In Proc. CVPR, 1738-1745. IEEE.

[12]

Hwang, T.; Tian, Z.; Kuangy, R.; and Kocher, J.-P. 2008. Learning on Weighted Hypergraphs to Integrate Protein Interactions and Gene Expressions for Cancer Outcome Prediction. In Proc. ICDM, 293-302. IEEE.

[13]

Kingma, D. P., and Ba, J. 2014. Adam: A Method for Stochastic Optimization. In Proc. ICLR.

[14]

Kipf, T. N., and Welling, M. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proc. ICLR.

[15]

Li, Y.; Bu, R.; Sun, M.; and Chen, B. 2018. PointCNN. In NIPS.

[16]

Li, J.; Chen, B. M.; and Lee, G. H. 2018. SO-Net: Self-Organizing Network for Point Cloud Analysis. In Proc. CVPR, 9397-9406.

[17]

Lu, Q., and Getoor, L. 2003. Link-based Classification. In Proc. ICML, 496-503.

[18]

Monti, F.; Boscaini, D.; Masci, J.; Rodola, E.; Svoboda, J.; and Bronstein, M. M. 2017. Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. In Proc. CVPR, volume 1, 3.

[19]

Perozzi, B.; Al-Rfou, R.; and Skiena, S. 2014. Deepwalk: Online Learning of Social Representations. In Proc. SIGKDD, 701-710. ACM.

[20]

Qi, C. R.; Su, H.; Mo, K.; and Guibas, L. J. 2017a. Point-Net: Deep Learning on Point Sets for 3D Classification and Segmentation. Proc. CVPR 1(2):4.

[21]

Qi, C. R.; Yi, L.; Su, H.; and Guibas, L. J. 2017b. Point-Net++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In NIPS, 5105-5114.

[22]

Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; and Monfardini, G. 2009. The Graph Neural Network Model. IEEE Transactions on Neural Networks 20(1):61-80.

Digital Library

[23]

Sen, P.; Namata, G.; Bilgic, M.; Getoor, L.; Galligher, B.; and Eliassi-Rad, T. 2008. Collective Classification in Network Data. AI magazine 29(3):93.

[24]

Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research 15(1): 1929-1958.

Digital Library

[25]

Su, H.; Maji, S.; Kalogerakis, E.; and Learned-Miller, E. 2015. Multi-View Convolutional Neural Networks for 3D Shape Recognition. In Proc. ICCV, 945-953.

[26]

Velickovic, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2018. Graph Attention networks. In Proc. ICLR, volume 1.

[27]

Wu, Z.; Song, S.; Khosla, A.; Yu, F.; Zhang, L.; Tang, X.; and Xiao, J. 2015. 3D ShapeNets: A Deep Representation for Volumetric Shapes. In Proc. CVPR, 1912-1920.

[28]

Yang, Z.; Cohen, W. W.; and Salakhutdinov, R. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. Proc. ICML.

[29]

Zhou, D.; Huang, J.; and Schölkopf, B. 2007. Learning with Hypergraphs: Clustering, Classification, and Embedding. In NIPS, 1601-1608.

Cited By

Li ZDing NLiang CCao SZhai MHuang RZhang ZHu B(2025)A semi-supervised framework fusing multiple information for knowledge graph entity alignmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125282259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125282
Papamarkou TBirdal TBronstein MCarlsson GCurry JGao YHajij MKwitt RLiò PDi Lorenzo PMaroulas VMiolane NNasrin FRamamurthy KRieck BScardapane SSchaub MVeličković PWang BWang YWei GZamzmi GSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693671(39529-39555)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693671
Ju WMao ZYi SQin YGu YXiao ZWang YLuo XZhang MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Hypergraph-enhanced dual semi-supervised graph classificationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692978(22594-22604)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692978
Show More Cited By

Index Terms

Hypergraph neural networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Hypergraph Based Berge Hypergraphs
Abstract
Fix a hypergraph $F$ . A hypergraph $H$ is called a Berge copy of $F$ or Berge- $F$ if we can choose a subset of each hyperedge of $H$ to obtain a copy of $F$ . A hypergraph $H$ is Berge- $F$ -free if it does not contain a subhypergraph which is Berge copy of $F$ . This ... $^{}$
Acyclic Hypergraph Projections

Hypergraphs can be partitioned into two classes: tree (acyclic) hypergraphs and cyclic hypergraphs. This paper analyzes a new class of cyclic hypergraphs called Xrings. HypergraphHis an Xring if the hyperedges ofHcan be circularly ordered so that for ...
Selective hypergraph colourings

We look at colourings of r -uniform hypergraphs, focusing our attention on unique colourability and gaps in the chromatic spectrum. The pattern of an edge E in an r -uniform hypergraph H whose vertices are coloured is the partition of r induced by the ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence

January 2019

10088 pages

ISBN:978-1-57735-809-1

Copyright © 2019 Association for the Advancement of Artificial Intelligence.

Sponsors

Association for the Advancement of Artificial Intelligence

Publisher

AAAI Press

Publication History

Published: 27 January 2019

Qualifiers

Research-article
Research
Refereed limited

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

173
Total Citations
View Citations
413
Total Downloads

Downloads (Last 12 months)286
Downloads (Last 6 weeks)51

Reflects downloads up to 11 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Li ZDing NLiang CCao SZhai MHuang RZhang ZHu B(2025)A semi-supervised framework fusing multiple information for knowledge graph entity alignmentExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125282259:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125282
Papamarkou TBirdal TBronstein MCarlsson GCurry JGao YHajij MKwitt RLiò PDi Lorenzo PMaroulas VMiolane NNasrin FRamamurthy KRieck BScardapane SSchaub MVeličković PWang BWang YWei GZamzmi GSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693671(39529-39555)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693671
Ju WMao ZYi SQin YGu YXiao ZWang YLuo XZhang MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Hypergraph-enhanced dual semi-supervised graph classificationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692978(22594-22604)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692978
Aminian GHe YReinert GSzpruch ŁCohen SSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Generalization error of graph neural networks in the mean-field regimeProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692127(1359-1391)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692127
Li FWang XCheng DZhang WZhang YLin XLarson K(2024)Hypergraph self-supervised learning with sampling-efficient signalsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/486(4398-4406)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/486
He XGe HHou YYu JLarson K(2024)SAEIRProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/454(4107-4115)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/454
Xu YFeng YGao YLarson K(2024)Negative prompt driven complementary parallel representation for open-world 3D object retrievalProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/166(1498-1506)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/166
Meng CPan CLi ZZhou CCao XZhu JLarson K(2024)DFRPProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/1022(8741-8744)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/1022
Guo ZLi JLi GWang CShi SRuan BWooldridge MDy JNatarajan S(2024)LGMRecProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28688(8454-8462)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i8.28688
Zhu KZhao CWooldridge MDy JNatarajan S(2024)Towards the disappearing truthProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i15.29662(17167-17174)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i15.29662
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents