Hypergraph convolution and hypergraph attention
•Hypergraph convolution defines a basic convolutional operator in a hypergraph. It enables an efficient information propagation between vertices by fully exploiting the high order relationship and local clustering structure therein. We mathematically prove that graph convolution is a special case of...
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| Vydané v: | Pattern recognition Ročník 110; s. 107637 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.02.2021
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| Predmet: | |
| ISSN: | 0031-3203, 1873-5142 |
| On-line prístup: | Získať plný text |
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| Abstract | •Hypergraph convolution defines a basic convolutional operator in a hypergraph. It enables an efficient information propagation between vertices by fully exploiting the high order relationship and local clustering structure therein. We mathematically prove that graph convolution is a special case of hypergraph convolution when the non pairwise relationship degenerates to a pairwise one.•Apart from hypergraph convolution where the underlying structure used for propagation is pre defined, hypergraph attention further exerts an attention mechanism to learn a dynamic connection of hyperedges. Then, the information propagation and gathering is done in task relevant parts of the graph, thereby generating more discriminative node embeddings.•Both hypergraph convolution and hypergraph attention are end to end trainable, and can be inserted into most variants of graph neural networks as long as non pairwise relationships are observed. Extensive experimental results on benchmark datasets demonstrate the efficacy of the proposed methods for semi supervised node classification.
Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention. |
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| AbstractList | •Hypergraph convolution defines a basic convolutional operator in a hypergraph. It enables an efficient information propagation between vertices by fully exploiting the high order relationship and local clustering structure therein. We mathematically prove that graph convolution is a special case of hypergraph convolution when the non pairwise relationship degenerates to a pairwise one.•Apart from hypergraph convolution where the underlying structure used for propagation is pre defined, hypergraph attention further exerts an attention mechanism to learn a dynamic connection of hyperedges. Then, the information propagation and gathering is done in task relevant parts of the graph, thereby generating more discriminative node embeddings.•Both hypergraph convolution and hypergraph attention are end to end trainable, and can be inserted into most variants of graph neural networks as long as non pairwise relationships are observed. Extensive experimental results on benchmark datasets demonstrate the efficacy of the proposed methods for semi supervised node classification.
Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention. |
| ArticleNumber | 107637 |
| Author | Torr, Philip H.S. Bai, Song Zhang, Feihu |
| Author_xml | – sequence: 1 givenname: Song orcidid: 0000-0002-2570-9118 surname: Bai fullname: Bai, Song email: songbai.site@gmail.com – sequence: 2 givenname: Feihu surname: Zhang fullname: Zhang, Feihu email: feihu.zhang@eng.ox.ac.uk – sequence: 3 givenname: Philip H.S. surname: Torr fullname: Torr, Philip H.S. email: philip.torr@eng.ox.ac.uk |
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| Cites_doi | 10.1109/MSP.2012.2205597 10.1109/TPAMI.2012.60 10.1080/0308108031000084374 10.1109/TNN.2008.2005605 10.1016/j.patcog.2014.09.002 10.1145/3363574 10.1109/43.784130 10.1016/j.patcog.2019.107040 10.1016/j.patcog.2008.12.029 10.1016/j.patcog.2013.01.004 10.1016/0012-365X(93)90322-K 10.1109/MSP.2017.2693418 10.24963/ijcai.2020/303 10.1016/j.patcog.2017.05.009 |
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| Copyright | 2020 Elsevier Ltd |
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| References | Chen, Zhu, Song (bib0033) 2018 Duvenaud, Maclaurin, Iparraguirre, Bombarell, Hirzel, Aspuru-Guzik, Adams (bib0023) 2015 Xu, Li, Tian, Sonobe, Kawarabayashi, Jegelka (bib0030) 2018 Lu, Getoor (bib0064) 2003 Gilmer, Schoenholz, Riley, Vinyals, Dahl (bib0027) 2017 You, Ying, Ren, Hamilton, Leskovec (bib0035) 2018 Ying, He, Chen, Eksombatchai, Hamilton, Leskovec (bib0008) 2018 P.W. Battaglia, J.B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al., Relational inductive biases, deep learning, and graph networks, arXiv Sen, Namata, Bilgic, Getoor, Galligher, Eliassi-Rad (bib0053) 2008; 29 Zhuang, Ma (bib0026) 2018 Chen, Ma, Xiao (bib0032) 2018 Hinton, Deng, Yu, Dahl, Mohamed, Jaitly, Senior, Vanhoucke, Nguyen, Sainath (bib0003) 2012; 29 Li, Chen, Koltun (bib0075) 2018 Zien, Schlag, Chan (bib0045) 1999; 18 Dai, Li, Tian, Huang, Wang, Zhu, Song (bib0037) 2018 R.K. Srivastava, K. Greff, J. Schmidhuber, Highway networks, arXiv Bahdanau, Cho, Bengio (bib0004) 2015 Pedronette, Valem, Almeida, Torres (bib0013) 2019; 28 M. Henaff, J. Bruna, Y. LeCun, Deep convolutional networks on graph-structured data, arXiv Li, Tarlow, Brockschmidt, Zemel (bib0028) 2016 Li, Milenkovic (bib0044) 2017 Maas, Hannun, Ng (bib0050) 2013 Bronstein, Bruna, LeCun, Szlam, Vandergheynst (bib0009) 2017; 34 Schlichtkrull, Kipf, Bloem, van den Berg, Titov, Welling (bib0066) 2018 Yu, Tao, Wang (bib0012) 2012; 21 (2015). Pham, Tran, Phung, Venkatesh (bib0029) 2017 Perozzi, Al-Rfou, Skiena (bib0063) 2014 B. Fatemi, P. Taslakian, D. Vazquez, D. Poole, Knowledge hypergraphs: prediction beyond binary relations, arXiv Jin, Yu, You, Zeng, Li, Yu (bib0074) 2015; 48 Bolla (bib0046) 1993; 117 He, Zhang, Ren, Sun (bib0002) 2016 C. Berge, Graphs and hypergraphs (1973). Velickovic, Cucurull, Casanova, Romero, Lio, Bengio (bib0031) 2018 Zhang, Hu, Tang, Chan (bib0011) 2017 (2019). Xie, Girshick, Dollár, Tu, He (bib0056) 2017 Scarselli, Gori, Tsoi, Hagenbuchner, Monfardini (bib0005) 2009; 20 Ying, You, Morris, Ren, Hamilton, Leskovec (bib0034) 2018 Weston, Ratle, Mobahi, Collobert (bib0061) 2012 Zhou, Huang, Schölkopf (bib0048) 2007 Yang, Cohen, Salakhutdinov (bib0058) 2016 Krizhevsky, Sutskever, Hinton (bib0001) 2012 Niepert, Ahmed, Kutzkov (bib0024) 2016 You, Liu, Ying, Pande, Leskovec (bib0067) 2018 Defferrard, Bresson, Vandergheynst (bib0021) 2016 Feng, You, Zhang, Ji, Gao (bib0049) 2019 Zhou, Bai, Liu, Zhou, Hancock (bib0070) 2020; 98 Rodriguez (bib0047) 2003; 51 Simonovsky, Komodakis (bib0065) 2017 Zhu, Ghahramani, Lafferty (bib0062) 2003 Monti, Boscaini, Masci, Rodola, Svoboda, Bronstein (bib0040) 2017 J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, M. Sun, Graph neural networks: a review of methods and applications, arXiv Kingma, Ba (bib0059) 2015 Pedronette, Gonçalves, Guilherme (bib0017) 2018; 75 Huang, Liu, Zhang, Metaxas (bib0052) 2010 Xiao, Hancock, Wilson (bib0068) 2009; 42 Zhang, Cui, Zhu (bib0014) 2020 Boscaini, Masci, Rodolà, Bronstein (bib0042) 2016 Agarwal, Branson, Belongie (bib0010) 2006 Xiao, Yi-Zhe, Hall (bib0069) 2011; 115 Hamilton, Ying, Leskovec (bib0006) 2017 Narasimhan, Lazebnik, Schwing (bib0071) 2018 Liu, Allamanis, Brockschmidt, Gaunt (bib0072) 2018 Kipf, Welling (bib0022) 2017 Belkin, Niyogi, Sindhwani (bib0060) 2006; 7 Atwood, Towsley (bib0025) 2016 Lee, Rossi, Kim, Ahmed, Koh (bib0039) 2019; 13 Rahimi, Cohn, Baldwin (bib0055) 2018 Yang, Prasad, Latecki (bib0016) 2012; 35 Wang, Ye, Gupta (bib0007) 2018 Bruna, Zaremba, Szlam, LeCun (bib0019) 2014 Bojchevski, Shchur, Zügner, Günnemann (bib0036) 2018 Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin (bib0057) 2017 (2018). Masci, Boscaini, Bronstein, Vandergheynst (bib0041) 2015 Pedronette, Torres (bib0018) 2013; 46 Clevert, Unterthiner, Hochreiter (bib0051) 2016 10.1016/j.patcog.2020.107637_bib0038 Feng (10.1016/j.patcog.2020.107637_bib0049) 2019 Liu (10.1016/j.patcog.2020.107637_bib0072) 2018 10.1016/j.patcog.2020.107637_bib0073 Kipf (10.1016/j.patcog.2020.107637_bib0022) 2017 Vaswani (10.1016/j.patcog.2020.107637_bib0057) 2017 Bahdanau (10.1016/j.patcog.2020.107637_bib0004) 2015 Hamilton (10.1016/j.patcog.2020.107637_bib0006) 2017 Bojchevski (10.1016/j.patcog.2020.107637_bib0036) 2018 Zhu (10.1016/j.patcog.2020.107637_bib0062) 2003 Li (10.1016/j.patcog.2020.107637_bib0075) 2018 Bolla (10.1016/j.patcog.2020.107637_bib0046) 1993; 117 Krizhevsky (10.1016/j.patcog.2020.107637_bib0001) 2012 You (10.1016/j.patcog.2020.107637_bib0035) 2018 Sen (10.1016/j.patcog.2020.107637_bib0053) 2008; 29 Zhang (10.1016/j.patcog.2020.107637_bib0014) 2020 Schlichtkrull (10.1016/j.patcog.2020.107637_bib0066) 2018 Weston (10.1016/j.patcog.2020.107637_bib0061) 2012 Velickovic (10.1016/j.patcog.2020.107637_bib0031) 2018 10.1016/j.patcog.2020.107637_bib0043 Ying (10.1016/j.patcog.2020.107637_bib0034) 2018 He (10.1016/j.patcog.2020.107637_bib0002) 2016 Kingma (10.1016/j.patcog.2020.107637_bib0059) 2015 Boscaini (10.1016/j.patcog.2020.107637_bib0042) 2016 Zhou (10.1016/j.patcog.2020.107637_bib0048) 2007 Zhuang (10.1016/j.patcog.2020.107637_bib0026) 2018 Duvenaud (10.1016/j.patcog.2020.107637_bib0023) 2015 Atwood (10.1016/j.patcog.2020.107637_bib0025) 2016 Pham (10.1016/j.patcog.2020.107637_bib0029) 2017 Xiao (10.1016/j.patcog.2020.107637_bib0069) 2011; 115 Wang (10.1016/j.patcog.2020.107637_bib0007) 2018 Simonovsky (10.1016/j.patcog.2020.107637_bib0065) 2017 Pedronette (10.1016/j.patcog.2020.107637_bib0013) 2019; 28 Perozzi (10.1016/j.patcog.2020.107637_bib0063) 2014 Scarselli (10.1016/j.patcog.2020.107637_bib0005) 2009; 20 Rahimi (10.1016/j.patcog.2020.107637_bib0055) 2018 Xu (10.1016/j.patcog.2020.107637_bib0030) 2018 Dai (10.1016/j.patcog.2020.107637_bib0037) 2018 Xiao (10.1016/j.patcog.2020.107637_bib0068) 2009; 42 Chen (10.1016/j.patcog.2020.107637_bib0032) 2018 Hinton (10.1016/j.patcog.2020.107637_bib0003) 2012; 29 Zhou (10.1016/j.patcog.2020.107637_bib0070) 2020; 98 Jin (10.1016/j.patcog.2020.107637_bib0074) 2015; 48 Bruna (10.1016/j.patcog.2020.107637_bib0019) 2014 Zhang (10.1016/j.patcog.2020.107637_bib0011) 2017 10.1016/j.patcog.2020.107637_bib0015 Belkin (10.1016/j.patcog.2020.107637_bib0060) 2006; 7 Maas (10.1016/j.patcog.2020.107637_bib0050) 2013 10.1016/j.patcog.2020.107637_bib0054 Lee (10.1016/j.patcog.2020.107637_bib0039) 2019; 13 Xie (10.1016/j.patcog.2020.107637_bib0056) 2017 Ying (10.1016/j.patcog.2020.107637_bib0008) 2018 You (10.1016/j.patcog.2020.107637_bib0067) 2018 Huang (10.1016/j.patcog.2020.107637_bib0052) 2010 Agarwal (10.1016/j.patcog.2020.107637_bib0010) 2006 Clevert (10.1016/j.patcog.2020.107637_bib0051) 2016 Bronstein (10.1016/j.patcog.2020.107637_bib0009) 2017; 34 Pedronette (10.1016/j.patcog.2020.107637_bib0018) 2013; 46 Gilmer (10.1016/j.patcog.2020.107637_bib0027) 2017 Yu (10.1016/j.patcog.2020.107637_bib0012) 2012; 21 Chen (10.1016/j.patcog.2020.107637_bib0033) 2018 Yang (10.1016/j.patcog.2020.107637_bib0016) 2012; 35 Defferrard (10.1016/j.patcog.2020.107637_bib0021) 2016 Li (10.1016/j.patcog.2020.107637_bib0028) 2016 Narasimhan (10.1016/j.patcog.2020.107637_bib0071) 2018 10.1016/j.patcog.2020.107637_bib0020 Masci (10.1016/j.patcog.2020.107637_bib0041) 2015 Pedronette (10.1016/j.patcog.2020.107637_bib0017) 2018; 75 Zien (10.1016/j.patcog.2020.107637_bib0045) 1999; 18 Monti (10.1016/j.patcog.2020.107637_bib0040) 2017 Lu (10.1016/j.patcog.2020.107637_bib0064) 2003 Yang (10.1016/j.patcog.2020.107637_bib0058) 2016 Niepert (10.1016/j.patcog.2020.107637_bib0024) 2016 Rodriguez (10.1016/j.patcog.2020.107637_bib0047) 2003; 51 Li (10.1016/j.patcog.2020.107637_bib0044) 2017 |
| References_xml | – start-page: 6857 year: 2018 end-page: 6866 ident: bib0007 article-title: Zero-shot recognition via semantic embeddings and knowledge graphs publication-title: CVPR – reference: J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, M. Sun, Graph neural networks: a review of methods and applications, arXiv: – start-page: 912 year: 2003 end-page: 919 ident: bib0062 article-title: Semi-supervised learning using gaussian fields and harmonic functions publication-title: ICML – volume: 51 start-page: 285 year: 2003 end-page: 297 ident: bib0047 article-title: On the Laplacian spectrum and walk-regular hypergraphs publication-title: Linear Multilinear Algebra – start-page: 701 year: 2014 end-page: 710 ident: bib0063 article-title: DeepWalk: online learning of social representations publication-title: KDD – volume: 42 start-page: 2589 year: 2009 end-page: 2606 ident: bib0068 article-title: Graph characteristics from the heat kernel trace publication-title: Pattern Recognit. – start-page: 2485 year: 2017 end-page: 2491 ident: bib0029 article-title: Column networks for collective classification. publication-title: AAAI – year: 2017 ident: bib0065 article-title: Dynamic edge-conditioned filters in convolutional neural networks on graphs publication-title: CVPR – reference: (2019). – year: 2014 ident: bib0019 article-title: Spectral networks and locally connected networks on graphs publication-title: ICLR – year: 2018 ident: bib0032 article-title: FastGCN: fast learning with graph convolutional networks via importance sampling publication-title: ICLR – year: 2018 ident: bib0037 article-title: Adversarial attack on graph structured data publication-title: ICML – volume: 13 start-page: 1 year: 2019 end-page: 25 ident: bib0039 article-title: Attention models in graphs: a survey publication-title: TKDD – year: 2016 ident: bib0028 article-title: Gated graph sequence neural networks publication-title: ICLR – year: 2013 ident: bib0050 article-title: Rectifier nonlinearities improve neural network acoustic models publication-title: ICML – start-page: 499 year: 2018 end-page: 508 ident: bib0026 article-title: Dual graph convolutional networks for graph-based semi-supervised classification publication-title: World Wide Web – year: 2018 ident: bib0067 article-title: Graph convolutional policy network for goal-directed molecular graph generation publication-title: NeurlPS – reference: P.W. Battaglia, J.B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al., Relational inductive biases, deep learning, and graph networks, arXiv: – start-page: 17 year: 2006 end-page: 24 ident: bib0010 article-title: Higher order learning with graphs publication-title: ICML – start-page: 2659 year: 2018 end-page: 2670 ident: bib0071 article-title: Out of the box: reasoning with graph convolution nets for factual visual question answering publication-title: NeurlPS – year: 2018 ident: bib0008 article-title: Graph convolutional neural networks for web-scale recommender systems publication-title: KDD – year: 2015 ident: bib0059 article-title: Adam: a method for stochastic optimization publication-title: ICLR – year: 2018 ident: bib0031 article-title: Graph attention networks publication-title: ICLR – year: 2018 ident: bib0030 article-title: Representation learning on graphs with jumping knowledge networks publication-title: ICML – year: 2018 ident: bib0036 article-title: NetGAN: generating graphs via random walks publication-title: ICML – start-page: 941 year: 2018 end-page: 949 ident: bib0033 article-title: Stochastic training of graph convolutional networks with variance reduction. publication-title: ICML – start-page: 3189 year: 2016 end-page: 3197 ident: bib0042 article-title: Learning shape correspondence with anisotropic convolutional neural networks publication-title: NeurlPS – start-page: 5998 year: 2017 end-page: 6008 ident: bib0057 article-title: Attention is all you need publication-title: NeurlPS – start-page: 1024 year: 2017 end-page: 1034 ident: bib0006 article-title: Inductive representation learning on large graphs publication-title: NeurlPS – start-page: 1097 year: 2012 end-page: 1105 ident: bib0001 article-title: ImageNet classification with deep convolutional neural networks publication-title: NeurlPS – reference: (2018). – reference: B. Fatemi, P. Taslakian, D. Vazquez, D. Poole, Knowledge hypergraphs: prediction beyond binary relations, arXiv: – year: 2015 ident: bib0004 article-title: Neural machine translation by jointly learning to align and translate publication-title: ICLR – volume: 18 start-page: 1389 year: 1999 end-page: 1399 ident: bib0045 article-title: Multilevel spectral hypergraph partitioning with arbitrary vertex sizes publication-title: IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. – volume: 75 start-page: 161 year: 2018 end-page: 174 ident: bib0017 article-title: Unsupervised manifold learning through reciprocal kNN graph and connected components for image retrieval tasks publication-title: Pattern Recognit. – start-page: 7806 year: 2018 end-page: 7815 ident: bib0072 article-title: Constrained graph variational autoencoders for molecule design publication-title: NeurlPS – start-page: 2224 year: 2015 end-page: 2232 ident: bib0023 article-title: Convolutional networks on graphs for learning molecular fingerprints publication-title: NeurlPS – start-page: 537 year: 2018 end-page: 546 ident: bib0075 article-title: Combinatorial optimization with graph convolutional networks and guided tree search publication-title: NeurlPS – start-page: 3844 year: 2016 end-page: 3852 ident: bib0021 article-title: Convolutional neural networks on graphs with fast localized spectral filtering publication-title: NeurlPS – start-page: 5987 year: 2017 end-page: 5995 ident: bib0056 article-title: Aggregated residual transformations for deep neural networks publication-title: CVPR – volume: 29 start-page: 82 year: 2012 end-page: 97 ident: bib0003 article-title: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups publication-title: IEEE Signal Process. Mag. – year: 2017 ident: bib0040 article-title: Geometric deep learning on graphs and manifolds using mixture model CNNs publication-title: CVPR – year: 2018 ident: bib0055 article-title: Semi-supervised user geolocation via graph convolutional networks publication-title: ACL – start-page: 1993 year: 2016 end-page: 2001 ident: bib0025 article-title: Diffusion-convolutional neural networks publication-title: NeurlPS – reference: (2015). – start-page: 37 year: 2015 end-page: 45 ident: bib0041 article-title: Geodesic convolutional neural networks on Riemannian manifolds publication-title: ICCVW – start-page: 593 year: 2018 end-page: 607 ident: bib0066 article-title: Modeling relational data with graph convolutional networks publication-title: European Semantic Web Conference – start-page: 770 year: 2016 end-page: 778 ident: bib0002 article-title: Deep residual learning for image recognition publication-title: CVPR – volume: 34 start-page: 18 year: 2017 end-page: 42 ident: bib0009 article-title: Geometric deep learning: going beyond euclidean data publication-title: IEEE Signal Process. Mag. – reference: M. Henaff, J. Bruna, Y. LeCun, Deep convolutional networks on graph-structured data, arXiv: – year: 2017 ident: bib0027 article-title: Neural message passing for quantum chemistry publication-title: ICML – year: 2019 ident: bib0049 article-title: Hypergraph neural networks publication-title: AAAI – volume: 46 start-page: 2350 year: 2013 end-page: 2360 ident: bib0018 article-title: Image re-ranking and rank aggregation based on similarity of ranked lists publication-title: Pattern Recognit. – volume: 20 start-page: 61 year: 2009 end-page: 80 ident: bib0005 article-title: The graph neural network model publication-title: IEEE Trans. Neural Netw. – start-page: 3376 year: 2010 end-page: 3383 ident: bib0052 article-title: Image retrieval via probabilistic hypergraph ranking publication-title: CVPR – volume: 29 start-page: 93 year: 2008 ident: bib0053 article-title: Collective classification in network data publication-title: AI Mag. – year: 2016 ident: bib0051 article-title: Fast and accurate deep network learning by exponential linear units (ELUs) publication-title: ICLR – volume: 115 start-page: 1023 year: 2011 end-page: 1031 ident: bib0069 article-title: Learning invariant structure for object identification by using graph methods publication-title: CVIU – volume: 35 start-page: 28 year: 2012 end-page: 38 ident: bib0016 article-title: Affinity learning with diffusion on tensor product graph publication-title: TPAMI – volume: 7 start-page: 2399 year: 2006 end-page: 2434 ident: bib0060 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – volume: 28 start-page: 5824 year: 2019 end-page: 5838 ident: bib0013 article-title: Multimedia retrieval through unsupervised hypergraph-based manifold ranking publication-title: TIP – start-page: 2014 year: 2016 end-page: 2023 ident: bib0024 article-title: Learning convolutional neural networks for graphs publication-title: ICML – start-page: 2308 year: 2017 end-page: 2318 ident: bib0044 article-title: Inhomogeneous hypergraph clustering with applications publication-title: NeurlPS – volume: 21 start-page: 3262 year: 2012 end-page: 3272 ident: bib0012 article-title: Adaptive hypergraph learning and its application in image classification publication-title: TIP – year: 2020 ident: bib0014 article-title: Deep learning on graphs: a survey publication-title: TKDE – start-page: 1601 year: 2007 end-page: 1608 ident: bib0048 article-title: Learning with hypergraphs: clustering, classification, and embedding publication-title: NeurlPS – start-page: 496 year: 2003 end-page: 503 ident: bib0064 article-title: Link-based classification publication-title: ICML – year: 2017 ident: bib0022 article-title: Semi-supervised classification with graph convolutional networks publication-title: ICLR – start-page: 4805 year: 2018 end-page: 4815 ident: bib0034 article-title: Hierarchical graph representation learning with differentiable pooling publication-title: NeurlPS – start-page: 5694 year: 2018 end-page: 5703 ident: bib0035 article-title: GraphRNN: generating realistic graphs with deep auto-regressive models publication-title: ICML – reference: C. Berge, Graphs and hypergraphs (1973). – volume: 117 start-page: 19 year: 1993 end-page: 39 ident: bib0046 article-title: Spectra, euclidean representations and clusterings of hypergraphs publication-title: Discrete Math. – start-page: 639 year: 2012 end-page: 655 ident: bib0061 article-title: Deep Learning via Semi-supervised Embedding publication-title: Neural Networks: Tricks of the Trade – year: 2016 ident: bib0058 article-title: Revisiting semi-supervised learning with graph embeddings publication-title: ICML – volume: 98 start-page: 107040 year: 2020 ident: bib0070 article-title: Learning binary code for fast nearest subspace search publication-title: Pattern Recognit. – volume: 48 start-page: 1011 year: 2015 end-page: 1022 ident: bib0074 article-title: Low-rank matrix factorization with multiple hypergraph regularizer publication-title: Pattern Recognit. – start-page: 4026 year: 2017 end-page: 4034 ident: bib0011 article-title: Re-revisiting learning on hypergraphs: confidence interval and subgradient method publication-title: ICML – reference: R.K. Srivastava, K. Greff, J. Schmidhuber, Highway networks, arXiv: – start-page: 5998 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0057 article-title: Attention is all you need – volume: 29 start-page: 82 issue: 6 year: 2012 ident: 10.1016/j.patcog.2020.107637_bib0003 article-title: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2012.2205597 – volume: 35 start-page: 28 issue: 1 year: 2012 ident: 10.1016/j.patcog.2020.107637_bib0016 article-title: Affinity learning with diffusion on tensor product graph publication-title: TPAMI doi: 10.1109/TPAMI.2012.60 – start-page: 37 year: 2015 ident: 10.1016/j.patcog.2020.107637_bib0041 article-title: Geodesic convolutional neural networks on Riemannian manifolds – volume: 51 start-page: 285 issue: 3 year: 2003 ident: 10.1016/j.patcog.2020.107637_bib0047 article-title: On the Laplacian spectrum and walk-regular hypergraphs publication-title: Linear Multilinear Algebra doi: 10.1080/0308108031000084374 – start-page: 912 year: 2003 ident: 10.1016/j.patcog.2020.107637_bib0062 article-title: Semi-supervised learning using gaussian fields and harmonic functions – volume: 21 start-page: 3262 issue: 7 year: 2012 ident: 10.1016/j.patcog.2020.107637_bib0012 article-title: Adaptive hypergraph learning and its application in image classification publication-title: TIP – start-page: 496 year: 2003 ident: 10.1016/j.patcog.2020.107637_bib0064 article-title: Link-based classification – start-page: 2659 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0071 article-title: Out of the box: reasoning with graph convolution nets for factual visual question answering – volume: 20 start-page: 61 issue: 1 year: 2009 ident: 10.1016/j.patcog.2020.107637_bib0005 article-title: The graph neural network model publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2008.2005605 – start-page: 1024 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0006 article-title: Inductive representation learning on large graphs – year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0022 article-title: Semi-supervised classification with graph convolutional networks – volume: 48 start-page: 1011 issue: 3 year: 2015 ident: 10.1016/j.patcog.2020.107637_bib0074 article-title: Low-rank matrix factorization with multiple hypergraph regularizer publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2014.09.002 – volume: 28 start-page: 5824 issue: 12 year: 2019 ident: 10.1016/j.patcog.2020.107637_bib0013 article-title: Multimedia retrieval through unsupervised hypergraph-based manifold ranking publication-title: TIP – volume: 13 start-page: 1 issue: 6 year: 2019 ident: 10.1016/j.patcog.2020.107637_bib0039 article-title: Attention models in graphs: a survey publication-title: TKDD doi: 10.1145/3363574 – volume: 18 start-page: 1389 issue: 9 year: 1999 ident: 10.1016/j.patcog.2020.107637_bib0045 article-title: Multilevel spectral hypergraph partitioning with arbitrary vertex sizes publication-title: IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. doi: 10.1109/43.784130 – start-page: 1601 year: 2007 ident: 10.1016/j.patcog.2020.107637_bib0048 article-title: Learning with hypergraphs: clustering, classification, and embedding – start-page: 537 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0075 article-title: Combinatorial optimization with graph convolutional networks and guided tree search – start-page: 5987 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0056 article-title: Aggregated residual transformations for deep neural networks – year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0058 article-title: Revisiting semi-supervised learning with graph embeddings – start-page: 4026 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0011 article-title: Re-revisiting learning on hypergraphs: confidence interval and subgradient method – start-page: 941 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0033 article-title: Stochastic training of graph convolutional networks with variance reduction. – year: 2015 ident: 10.1016/j.patcog.2020.107637_bib0004 article-title: Neural machine translation by jointly learning to align and translate – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0031 article-title: Graph attention networks – year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0028 article-title: Gated graph sequence neural networks – ident: 10.1016/j.patcog.2020.107637_bib0015 – year: 2019 ident: 10.1016/j.patcog.2020.107637_bib0049 article-title: Hypergraph neural networks – ident: 10.1016/j.patcog.2020.107637_bib0043 – start-page: 1993 year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0025 article-title: Diffusion-convolutional neural networks – volume: 98 start-page: 107040 year: 2020 ident: 10.1016/j.patcog.2020.107637_bib0070 article-title: Learning binary code for fast nearest subspace search publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2019.107040 – volume: 7 start-page: 2399 year: 2006 ident: 10.1016/j.patcog.2020.107637_bib0060 article-title: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples publication-title: J. Mach. Learn. Res. – year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0027 article-title: Neural message passing for quantum chemistry – start-page: 639 year: 2012 ident: 10.1016/j.patcog.2020.107637_bib0061 article-title: Deep Learning via Semi-supervised Embedding – year: 2020 ident: 10.1016/j.patcog.2020.107637_bib0014 article-title: Deep learning on graphs: a survey publication-title: TKDE – ident: 10.1016/j.patcog.2020.107637_bib0054 – start-page: 3189 year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0042 article-title: Learning shape correspondence with anisotropic convolutional neural networks – volume: 42 start-page: 2589 issue: 11 year: 2009 ident: 10.1016/j.patcog.2020.107637_bib0068 article-title: Graph characteristics from the heat kernel trace publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2008.12.029 – volume: 46 start-page: 2350 issue: 8 year: 2013 ident: 10.1016/j.patcog.2020.107637_bib0018 article-title: Image re-ranking and rank aggregation based on similarity of ranked lists publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2013.01.004 – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0032 article-title: FastGCN: fast learning with graph convolutional networks via importance sampling – start-page: 1097 year: 2012 ident: 10.1016/j.patcog.2020.107637_bib0001 article-title: ImageNet classification with deep convolutional neural networks – volume: 117 start-page: 19 issue: 1–3 year: 1993 ident: 10.1016/j.patcog.2020.107637_bib0046 article-title: Spectra, euclidean representations and clusterings of hypergraphs publication-title: Discrete Math. doi: 10.1016/0012-365X(93)90322-K – year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0065 article-title: Dynamic edge-conditioned filters in convolutional neural networks on graphs – start-page: 2485 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0029 article-title: Column networks for collective classification. – year: 2015 ident: 10.1016/j.patcog.2020.107637_bib0059 article-title: Adam: a method for stochastic optimization – start-page: 593 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0066 article-title: Modeling relational data with graph convolutional networks – volume: 34 start-page: 18 issue: 4 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0009 article-title: Geometric deep learning: going beyond euclidean data publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2017.2693418 – start-page: 3844 year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0021 article-title: Convolutional neural networks on graphs with fast localized spectral filtering – start-page: 5694 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0035 article-title: GraphRNN: generating realistic graphs with deep auto-regressive models – start-page: 2308 year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0044 article-title: Inhomogeneous hypergraph clustering with applications – year: 2014 ident: 10.1016/j.patcog.2020.107637_bib0019 article-title: Spectral networks and locally connected networks on graphs – volume: 29 start-page: 93 issue: 3 year: 2008 ident: 10.1016/j.patcog.2020.107637_bib0053 article-title: Collective classification in network data publication-title: AI Mag. – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0067 article-title: Graph convolutional policy network for goal-directed molecular graph generation – start-page: 17 year: 2006 ident: 10.1016/j.patcog.2020.107637_bib0010 article-title: Higher order learning with graphs – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0036 article-title: NetGAN: generating graphs via random walks – start-page: 2224 year: 2015 ident: 10.1016/j.patcog.2020.107637_bib0023 article-title: Convolutional networks on graphs for learning molecular fingerprints – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0008 article-title: Graph convolutional neural networks for web-scale recommender systems – year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0051 article-title: Fast and accurate deep network learning by exponential linear units (ELUs) – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0037 article-title: Adversarial attack on graph structured data – ident: 10.1016/j.patcog.2020.107637_bib0073 doi: 10.24963/ijcai.2020/303 – year: 2017 ident: 10.1016/j.patcog.2020.107637_bib0040 article-title: Geometric deep learning on graphs and manifolds using mixture model CNNs – start-page: 3376 year: 2010 ident: 10.1016/j.patcog.2020.107637_bib0052 article-title: Image retrieval via probabilistic hypergraph ranking – volume: 115 start-page: 1023 issue: 7 year: 2011 ident: 10.1016/j.patcog.2020.107637_bib0069 article-title: Learning invariant structure for object identification by using graph methods publication-title: CVIU – ident: 10.1016/j.patcog.2020.107637_bib0038 – year: 2013 ident: 10.1016/j.patcog.2020.107637_bib0050 article-title: Rectifier nonlinearities improve neural network acoustic models – start-page: 499 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0026 article-title: Dual graph convolutional networks for graph-based semi-supervised classification – start-page: 6857 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0007 article-title: Zero-shot recognition via semantic embeddings and knowledge graphs – start-page: 4805 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0034 article-title: Hierarchical graph representation learning with differentiable pooling – start-page: 701 year: 2014 ident: 10.1016/j.patcog.2020.107637_bib0063 article-title: DeepWalk: online learning of social representations – start-page: 7806 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0072 article-title: Constrained graph variational autoencoders for molecule design – volume: 75 start-page: 161 year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0017 article-title: Unsupervised manifold learning through reciprocal kNN graph and connected components for image retrieval tasks publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.05.009 – start-page: 2014 year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0024 article-title: Learning convolutional neural networks for graphs – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0055 article-title: Semi-supervised user geolocation via graph convolutional networks – start-page: 770 year: 2016 ident: 10.1016/j.patcog.2020.107637_bib0002 article-title: Deep residual learning for image recognition – ident: 10.1016/j.patcog.2020.107637_bib0020 – year: 2018 ident: 10.1016/j.patcog.2020.107637_bib0030 article-title: Representation learning on graphs with jumping knowledge networks |
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