AEGCN: An Autoencoder-Constrained Graph Convolutional Network
We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constraine...
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| Published in: | Neurocomputing (Amsterdam) Vol. 432; pp. 21 - 31 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
07.04.2021
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constrained by an autoencoder. Comparing with vanilla graph convolutional networks, the autoencoder step is added to reduce the information loss brought by Laplacian smoothing. We consider applying our model on both homogeneous graphs and heterogeneous graphs. For homogeneous graphs, the autoencoder approximates to the adjacency matrix of the input graph by taking hidden layer representations as encoder and another one-layer graph convolutional network as decoder. For heterogeneous graphs, since there are multiple adjacency matrices corresponding to different types of edges, the autoencoder approximates to the feature matrix of the input graph instead, and changes the encoder to a particularly designed multi-channel pre-processing network with two layers. In both cases, the error occurred in the autoencoder approximation goes to the penalty term in the loss function. In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks. Further, we notice that our technique can be applied on graph attention network to improve the performance as well. This reveals the wide applicability of the proposed autoencoder technique. |
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| AbstractList | We propose a novel neural network architecture, called autoencoder-constrained graph convolutional network, to solve node classification task on graph domains. As suggested by its name, the core of this model is a convolutional network operating directly on graphs, whose hidden layers are constrained by an autoencoder. Comparing with vanilla graph convolutional networks, the autoencoder step is added to reduce the information loss brought by Laplacian smoothing. We consider applying our model on both homogeneous graphs and heterogeneous graphs. For homogeneous graphs, the autoencoder approximates to the adjacency matrix of the input graph by taking hidden layer representations as encoder and another one-layer graph convolutional network as decoder. For heterogeneous graphs, since there are multiple adjacency matrices corresponding to different types of edges, the autoencoder approximates to the feature matrix of the input graph instead, and changes the encoder to a particularly designed multi-channel pre-processing network with two layers. In both cases, the error occurred in the autoencoder approximation goes to the penalty term in the loss function. In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks. Further, we notice that our technique can be applied on graph attention network to improve the performance as well. This reveals the wide applicability of the proposed autoencoder technique. |
| Author | Wang, Hongyu Na, Sen Ma, Mingyuan |
| Author_xml | – sequence: 1 givenname: Mingyuan surname: Ma fullname: Ma, Mingyuan organization: School of Electronics Engineering and Computer Science, Peking University, Beijing, China – sequence: 2 givenname: Sen surname: Na fullname: Na, Sen organization: Department of Statistics, University of Chicago, Chicago, IL, USA – sequence: 3 givenname: Hongyu surname: Wang fullname: Wang, Hongyu email: why5126@pku.edu.cn organization: School of Electronics Engineering and Computer Science, Peking University, Beijing, China |
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| Cites_doi | 10.1021/ci00047a033 10.1109/TSP.2016.2602809 10.1002/prot.1081 10.1109/78.650093 10.1093/genetics/154.2.923 10.1073/pnas.0601602103 10.1109/TPAMI.2010.231 10.1016/j.physrep.2009.11.002 10.1109/TKDE.2018.2807452 10.24963/ijcai.2018/362 10.1093/bioinformatics/btn079 10.1609/aaai.v32i1.11604 10.1109/TITS.2019.2910560 10.1016/0022-2836(90)90312-A 10.1609/aaai.v28i1.8916 10.1609/aaai.v32i1.11691 10.1609/aaai.v34i01.5414 10.1609/aimag.v29i3.2157 10.1007/978-1-4614-1800-9_178 10.1111/j.1752-4571.2008.00047.x 10.14778/3402707.3402736 10.1021/cr00070a005 10.1145/2500492 10.1145/3397271.3401063 10.1145/2736277.2741093 10.1007/s10618-010-0210-x |
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| Keywords | Graph autoencoder Graph node classification Graph convolutional networks Homogeneous and heterogeneous graphs |
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| References | Sun, Han, Yan, Yu, Wu, Pathsim (b0335) 2011; 4 Jacobs, Rader, Kuhn, Thorpe (b0010) 2001; 44 Gao, Denoyer, Gallinari (b0060) 2011 T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv:1609.02907, 2016. Dong, Chawla, Swami, metapath2vec (b0115) 2017 R. v. d. Berg, T.N. Kipf, M. Welling, Graph convolutional matrix completion, arXiv preprint arXiv:1706.02263, 2017. Schuster, Paliwal (b0185) 1997; 45 Fu, Lee, Lei, Hin2vec (b0120) 2017 Hamilton, Ying, Leskovec (b0305) 2017 van der Maaten, Hinton (b0085) 2008; 9 Qiu, Dong, Ma, Li, Wang, Tang (b0170) 2018 Higham, Rašajski, Pržulj (b0015) 2008; 24 J. Ugander, B. Karrer, L. Backstrom, C. Marlow, The anatomy of the facebook social graph, arXiv preprint arXiv:1111.4503, 2011. Tang, Qu, Mei (b0110) 2015 T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781, 2013. Seo, Defferrard, Vandergheynst, Bresson (b0215) 2018 A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, in: Advances in Neural Information Processing Systems, 2017, pp. 5998–6008. A. Hasanzadeh, E. Hajiramezanali, K. Narayanan, N. Duffield, M. Zhou, X. Qian, Semi-implicit graph variational auto-encoders, in: Advances in Neural Information Processing Systems, 2019, pp. 10712–10723. Kampffmeyer, Chen, Liang, Wang, Zhang, Xing (b0310) 2019 F. Tian, B. Gao, Q. Cui, E. Chen, T.-Y. Liu, Learning deep representations for graph clustering, in: Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014. Grover, Leskovec (b0100) 2016 Newman (b0145) 2006; 103 J. Chen, T. Ma, C. Xiao, Fastgcn: fast learning with graph convolutional networks via importance sampling, arXiv preprint arXiv:1801.10247, 2018. Mitchell, Artymiuk, Rice, Willett (b0005) 1990; 212 Weston, Ratle, Mobahi, Collobert (b0385) 2012 Garroway, Bowman, Carr, Wilson (b0035) 2008; 1 Sen, Namata, Bilgic, Getoor, Galligher, Eliassi-Rad (b0345) 2008; 29 Wang, Ji, Shi, Wang, Ye, Cui, Yu (b0400) 2019 P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, arXiv preprint arXiv:1710.10903, 2017. R. Li, S. Wang, F. Zhu, J. Huang, Adaptive graph convolutional neural networks, in: Thirty-second AAAI Conference on Artificial Intelligence, 2018. J. You, R. Ying, X. Ren, W.L. Hamilton, J. Leskovec, Graphrnn: Generating realistic graphs with deep auto-regressive models, arXiv preprint arXiv:1802.08773, 2018. B. Huang, K.M. Carley, Residual or gate? towards deeper graph neural networks for inductive graph representation learning, arXiv preprint arXiv:1904.08035, 2019. T.N. Kipf, M. Welling, Variational graph auto-encoders, arXiv preprint arXiv:1611.07308, 2016. A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105. Zhu, Zhang, Cui, Zhu (b0390) 2019 Ying, He, Chen, Eksombatchai, Hamilton, Leskovec (b0235) 2018 Tang, Aggarwal, Liu (b0070) 2016 Cai, He, Han, Huang (b0150) 2011; 33 K. Xu, C. Li, Y. Tian, T. Sonobe, K.-I. Kawarabayashi, S. Jegelka, Representation learning on graphs with jumping knowledge networks, arXiv preprint arXiv:1806.03536, 2018. Yu, Gu (b0355) 2019; 20 Al Hasan, Zaki (b0055) 2011 Bhagat, Cormode, Muthukrishnan (b0065) 2011 Cai, Zheng, Chang (b0130) 2018; 30 Q. Li, Z. Han, X.-M. Wu, Deeper insights into graph convolutional networks for semi-supervised learning, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018. Nordborg (b0030) 2000; 154 Sun, Norick, Han, Yan, Yu, Yu (b0340) 2013; 7 Park, Lee, Chang, Lee, Choi (b0295) 2019 Tang, Liu (b0135) 2009 Yang, Sun, Liu, Tu (b0160) 2017 Taubin (b0350) 1995 Ou, Cui, Pei, Zhang, Zhu (b0165) 2016 Gao, Wang, Ji (b0230) 2018 F. Wu, T. Zhang, A.H. d. Souza Jr, C. Fifty, T. Yu, K.Q. Weinberger, Simplifying graph convolutional networks, arXiv preprint arXiv:1902.07153, 2019. X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, M. Wang, Lightgcn: Simplifying and powering graph convolution network for recommendation, arXiv preprint arXiv:2002.02126, 2020. Perozzi, Al-Rfou, Skiena (b0090) 2014 J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, Line: Large-scale information network embedding, in: Proceedings of the 24th international conference on world wide web, 2015, pp. 1067–1077. Zhao, Song, Zhang, Liu, Wang, Lin, Deng, Li (b0255) 2019 Ding, Tian, Lei, Liao, Wu (b0380) 2020 Yun, Jeong, Kim, Kang, Kim (b0365) 2019 Wang, Wang, Yang, Chang, Tsai (b0125) 2017 Dong, Thanou, Frossard, Vandergheynst (b0155) 2016; 64 Derr, Ma, Tang (b0225) 2018 S. Abu-El-Haija, A. Kapoor, B. Perozzi, J. Lee, N-gcn: Multi-scale graph convolution for semi-supervised node classification, in: Uncertainty in Artificial Intelligence, PMLR, 2020, pp. 841–851. B. Taskar, M.-F. Wong, P. Abbeel, D. Koller, Link prediction in relational data, in: Advances in neural information processing systems, 2004, pp. 659–666. Tang, Liu (b0140) 2011; 23 Zhou, Cui, Zhang, Yang, Liu, Wang, Li, Sun (b0220) 2018 S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, C. Zhang, Adversarially regularized graph autoencoder for graph embedding, arXiv preprint arXiv:1802.04407, 2018. S. Abu-El-Haija, A. Kapoor, B. Perozzi, J. Lee, N-gcn: Multi-scale graph convolution for semi-supervised node classification, arXiv preprint arXiv:1802.08888, 2018. Y. Li, D. Tarlow, M. Brockschmidt, R. Zemel, Gated graph sequence neural networks, arXiv preprint arXiv:1511.05493, 2015. Wang, Pan, Long, Zhu, Jiang, Mgae (b0285) 2017 Na, Luo, Yang, Wang, Kolar (b0300) 2020; 2020 Z. Ke, H. Vikalo, A graph auto-encoder for haplotype assembly and viral quasispecies reconstruction, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 719–726. Li, Muller, Thabet, Ghanem, Deepgcns (b0320) 2019 Liu, Murata, Kim, Kotarasu, Zhuang (b0175) 2019 Balaban (b0025) 1985; 25 J. Scott, Social network analysis, overview of, in: Computational complexity, vols. 1–6, Springer, New York, 2012, pp. 2898–2911. Balasubramanian (b0020) 1985; 85 Fortunato (b0080) 2010; 486 Yang, Cohen, Salakhudinov (b0395) 2016 Yan, Ai, Yang, Tong (b0360) 2020 Balaban (10.1016/j.neucom.2020.12.061_b0025) 1985; 25 Yun (10.1016/j.neucom.2020.12.061_b0365) 2019 Yu (10.1016/j.neucom.2020.12.061_b0355) 2019; 20 Balasubramanian (10.1016/j.neucom.2020.12.061_b0020) 1985; 85 Garroway (10.1016/j.neucom.2020.12.061_b0035) 2008; 1 Tang (10.1016/j.neucom.2020.12.061_b0140) 2011; 23 Wang (10.1016/j.neucom.2020.12.061_b0285) 2017 Mitchell (10.1016/j.neucom.2020.12.061_b0005) 1990; 212 10.1016/j.neucom.2020.12.061_b0325 10.1016/j.neucom.2020.12.061_b0205 10.1016/j.neucom.2020.12.061_b0200 10.1016/j.neucom.2020.12.061_b0245 Ou (10.1016/j.neucom.2020.12.061_b0165) 2016 10.1016/j.neucom.2020.12.061_b0045 10.1016/j.neucom.2020.12.061_b0240 10.1016/j.neucom.2020.12.061_b0040 Weston (10.1016/j.neucom.2020.12.061_b0385) 2012 10.1016/j.neucom.2020.12.061_b0280 Li (10.1016/j.neucom.2020.12.061_b0320) 2019 Jacobs (10.1016/j.neucom.2020.12.061_b0010) 2001; 44 Cai (10.1016/j.neucom.2020.12.061_b0130) 2018; 30 Perozzi (10.1016/j.neucom.2020.12.061_b0090) 2014 Hamilton (10.1016/j.neucom.2020.12.061_b0305) 2017 Zhou (10.1016/j.neucom.2020.12.061_b0220) 2018 Ying (10.1016/j.neucom.2020.12.061_b0235) 2018 Kampffmeyer (10.1016/j.neucom.2020.12.061_b0310) 2019 Ding (10.1016/j.neucom.2020.12.061_b0380) 2020 Dong (10.1016/j.neucom.2020.12.061_b0155) 2016; 64 Nordborg (10.1016/j.neucom.2020.12.061_b0030) 2000; 154 10.1016/j.neucom.2020.12.061_b0330 10.1016/j.neucom.2020.12.061_b0210 10.1016/j.neucom.2020.12.061_b0375 10.1016/j.neucom.2020.12.061_b0095 10.1016/j.neucom.2020.12.061_b0370 10.1016/j.neucom.2020.12.061_b0250 10.1016/j.neucom.2020.12.061_b0050 Tang (10.1016/j.neucom.2020.12.061_b0110) 2015 Al Hasan (10.1016/j.neucom.2020.12.061_b0055) 2011 van der Maaten (10.1016/j.neucom.2020.12.061_b0085) 2008; 9 10.1016/j.neucom.2020.12.061_b0290 Zhu (10.1016/j.neucom.2020.12.061_b0390) 2019 Sen (10.1016/j.neucom.2020.12.061_b0345) 2008; 29 Na (10.1016/j.neucom.2020.12.061_b0300) 2020; 2020 Tang (10.1016/j.neucom.2020.12.061_b0135) 2009 Liu (10.1016/j.neucom.2020.12.061_b0175) 2019 Fu (10.1016/j.neucom.2020.12.061_b0120) 2017 Park (10.1016/j.neucom.2020.12.061_b0295) 2019 10.1016/j.neucom.2020.12.061_b0105 Wang (10.1016/j.neucom.2020.12.061_b0400) 2019 Taubin (10.1016/j.neucom.2020.12.061_b0350) 1995 10.1016/j.neucom.2020.12.061_b0265 10.1016/j.neucom.2020.12.061_b0260 Gao (10.1016/j.neucom.2020.12.061_b0060) 2011 10.1016/j.neucom.2020.12.061_b0180 Seo (10.1016/j.neucom.2020.12.061_b0215) 2018 Sun (10.1016/j.neucom.2020.12.061_b0340) 2013; 7 Cai (10.1016/j.neucom.2020.12.061_b0150) 2011; 33 Sun (10.1016/j.neucom.2020.12.061_b0335) 2011; 4 Yang (10.1016/j.neucom.2020.12.061_b0160) 2017 Wang (10.1016/j.neucom.2020.12.061_b0125) 2017 Qiu (10.1016/j.neucom.2020.12.061_b0170) 2018 Yan (10.1016/j.neucom.2020.12.061_b0360) 2020 Derr (10.1016/j.neucom.2020.12.061_b0225) 2018 10.1016/j.neucom.2020.12.061_b0315 Yang (10.1016/j.neucom.2020.12.061_b0395) 2016 Schuster (10.1016/j.neucom.2020.12.061_b0185) 1997; 45 Gao (10.1016/j.neucom.2020.12.061_b0230) 2018 10.1016/j.neucom.2020.12.061_b0275 Dong (10.1016/j.neucom.2020.12.061_b0115) 2017 10.1016/j.neucom.2020.12.061_b0075 Higham (10.1016/j.neucom.2020.12.061_b0015) 2008; 24 Bhagat (10.1016/j.neucom.2020.12.061_b0065) 2011 10.1016/j.neucom.2020.12.061_b0195 Newman (10.1016/j.neucom.2020.12.061_b0145) 2006; 103 Zhao (10.1016/j.neucom.2020.12.061_b0255) 2019 Tang (10.1016/j.neucom.2020.12.061_b0070) 2016 10.1016/j.neucom.2020.12.061_b0270 Grover (10.1016/j.neucom.2020.12.061_b0100) 2016 10.1016/j.neucom.2020.12.061_b0190 Fortunato (10.1016/j.neucom.2020.12.061_b0080) 2010; 486 |
| References_xml | – volume: 9 start-page: 2579 year: 2008 end-page: 2605 ident: b0085 article-title: Visualizing data using t-SNE publication-title: Journal of Machine Learning Research – reference: T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781, 2013. – reference: A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105. – start-page: 1169 year: 2011 end-page: 1174 ident: b0060 article-title: Temporal link prediction by integrating content and structure information publication-title: Proceedings of the 20th ACM International Conference on Information and Knowledge Management – reference: S. Abu-El-Haija, A. Kapoor, B. Perozzi, J. Lee, N-gcn: Multi-scale graph convolution for semi-supervised node classification, arXiv preprint arXiv:1802.08888, 2018. – reference: Y. Li, D. Tarlow, M. Brockschmidt, R. Zemel, Gated graph sequence neural networks, arXiv preprint arXiv:1511.05493, 2015. – reference: X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, M. Wang, Lightgcn: Simplifying and powering graph convolution network for recommendation, arXiv preprint arXiv:2002.02126, 2020. – volume: 2020 year: 2020 ident: b0300 article-title: Semiparametric nonlinear bipartite graph representation learning with provable guarantees publication-title: International Conference on Machine Learning (ICML) – reference: R. Li, S. Wang, F. Zhu, J. Huang, Adaptive graph convolutional neural networks, in: Thirty-second AAAI Conference on Artificial Intelligence, 2018. – volume: 4 start-page: 992 year: 2011 end-page: 1003 ident: b0335 article-title: Meta path-based top-k similarity search in heterogeneous information networks publication-title: Proceedings of the VLDB Endowment – start-page: 817 year: 2009 end-page: 826 ident: b0135 article-title: Relational learning via latent social dimensions publication-title: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 45 start-page: 2673 year: 1997 end-page: 2681 ident: b0185 article-title: Bidirectional recurrent neural networks publication-title: IEEE Transactions on Signal Processing – start-page: 1 year: 2020 end-page: 23 ident: b0360 article-title: Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps publication-title: International Journal of Geographical Information Science – volume: 154 start-page: 923 year: 2000 end-page: 929 ident: b0030 article-title: Linkage disequilibrium, gene trees and selfing: an ancestral recombination graph with partial self-fertilization publication-title: Genetics – start-page: 1165 year: 2015 end-page: 1174 ident: b0110 article-title: Pte: Predictive text embedding through large-scale heterogeneous text networks publication-title: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – reference: R. v. d. Berg, T.N. Kipf, M. Welling, Graph convolutional matrix completion, arXiv preprint arXiv:1706.02263, 2017. – reference: J. Ugander, B. Karrer, L. Backstrom, C. Marlow, The anatomy of the facebook social graph, arXiv preprint arXiv:1111.4503, 2011. – start-page: 375 year: 2019 end-page: 383 ident: b0175 article-title: A general view for network embedding as matrix factorization publication-title: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining – reference: S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, C. Zhang, Adversarially regularized graph autoencoder for graph embedding, arXiv preprint arXiv:1802.04407, 2018. – reference: F. Tian, B. Gao, Q. Cui, E. Chen, T.-Y. Liu, Learning deep representations for graph clustering, in: Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014. – volume: 30 start-page: 1616 year: 2018 end-page: 1637 ident: b0130 article-title: A comprehensive survey of graph embedding: problems, techniques, and applications publication-title: IEEE Transactions on Knowledge and Data Engineering – reference: A. Hasanzadeh, E. Hajiramezanali, K. Narayanan, N. Duffield, M. Zhou, X. Qian, Semi-implicit graph variational auto-encoders, in: Advances in Neural Information Processing Systems, 2019, pp. 10712–10723. – start-page: 11983 year: 2019 end-page: 11993 ident: b0365 article-title: Graph transformer networks publication-title: Advances in Neural Information Processing Systems – volume: 25 start-page: 334 year: 1985 end-page: 343 ident: b0025 article-title: Applications of graph theory in chemistry publication-title: Journal of Chemical Information and Computer Sciences – start-page: 85 year: 2017 end-page: 94 ident: b0125 article-title: Ice: Item concept embedding via textual information publication-title: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval – reference: J. You, R. Ying, X. Ren, W.L. Hamilton, J. Leskovec, Graphrnn: Generating realistic graphs with deep auto-regressive models, arXiv preprint arXiv:1802.08773, 2018. – start-page: 459 year: 2018 end-page: 467 ident: b0170 article-title: Network embedding as matrix factorization: unifying deepwalk, line, pte, and node2vec publication-title: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining – reference: B. Huang, K.M. Carley, Residual or gate? towards deeper graph neural networks for inductive graph representation learning, arXiv preprint arXiv:1904.08035, 2019. – volume: 212 start-page: 151 year: 1990 end-page: 166 ident: b0005 article-title: Use of techniques derived from graph theory to compare secondary structure motifs in proteins publication-title: Journal of Molecular Biology – reference: T.N. Kipf, M. Welling, Variational graph auto-encoders, arXiv preprint arXiv:1611.07308, 2016. – year: 2020 ident: b0380 article-title: Variational graph auto-encoders for mirna-disease association prediction publication-title: Methods – volume: 486 start-page: 75 year: 2010 end-page: 174 ident: b0080 article-title: Community detection in graphs publication-title: Physics reports – volume: 7 start-page: 1 year: 2013 end-page: 23 ident: b0340 article-title: Pathselclus: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks publication-title: ACM Transactions on Knowledge Discovery from Data (TKDD) – start-page: 701 year: 2014 end-page: 710 ident: b0090 article-title: Deepwalk: Online learning of social representations publication-title: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 20 start-page: 3940 year: 2019 end-page: 3951 ident: b0355 article-title: Real-time traffic speed estimation with graph convolutional generative autoencoder publication-title: IEEE Transactions on Intelligent Transportation Systems – volume: 33 start-page: 1548 year: 2011 end-page: 1560 ident: b0150 article-title: Graph regularized nonnegative matrix factorization for data representation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – start-page: 11487 year: 2019 end-page: 11496 ident: b0310 article-title: Rethinking knowledge graph propagation for zero-shot learning publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – start-page: 54 year: 2016 end-page: 62 ident: b0070 article-title: Node classification in signed social networks publication-title: Proceedings of the 2016 SIAM International Conference on Data Mining – start-page: 639 year: 2012 end-page: 655 ident: b0385 article-title: Deep learning via semi-supervised embedding publication-title: Neural Networks: Tricks of the Trade – volume: 44 start-page: 150 year: 2001 end-page: 165 ident: b0010 article-title: Protein flexibility predictions using graph theory publication-title: Proteins: Structure, Function, and Bioinformatics – reference: Q. Li, Z. Han, X.-M. Wu, Deeper insights into graph convolutional networks for semi-supervised learning, in: Thirty-Second AAAI Conference on Artificial Intelligence, 2018. – start-page: 362 year: 2018 end-page: 373 ident: b0215 article-title: Structured sequence modeling with graph convolutional recurrent networks publication-title: International Conference on Neural Information Processing – start-page: 1399 year: 2019 end-page: 1407 ident: b0390 article-title: Robust graph convolutional networks against adversarial attacks publication-title: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – reference: B. Taskar, M.-F. Wong, P. Abbeel, D. Koller, Link prediction in relational data, in: Advances in neural information processing systems, 2004, pp. 659–666. – start-page: 1416 year: 2018 end-page: 1424 ident: b0230 article-title: Large-scale learnable graph convolutional networks publication-title: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – volume: 24 start-page: 1093 year: 2008 end-page: 1099 ident: b0015 article-title: Fitting a geometric graph to a protein–protein interaction network publication-title: Bioinformatics – start-page: 135 year: 2017 end-page: 144 ident: b0115 article-title: Scalable representation learning for heterogeneous networks publication-title: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 1 start-page: 620 year: 2008 end-page: 630 ident: b0035 article-title: Applications of graph theory to landscape genetics publication-title: Evolutionary Applications – volume: 64 start-page: 6160 year: 2016 end-page: 6173 ident: b0155 article-title: Learning laplacian matrix in smooth graph signal representations publication-title: IEEE Transactions on Signal Processing – reference: S. Abu-El-Haija, A. Kapoor, B. Perozzi, J. Lee, N-gcn: Multi-scale graph convolution for semi-supervised node classification, in: Uncertainty in Artificial Intelligence, PMLR, 2020, pp. 841–851. – start-page: 6519 year: 2019 end-page: 6528 ident: b0295 article-title: Symmetric graph convolutional autoencoder for unsupervised graph representation learning publication-title: Proceedings of the IEEE International Conference on Computer Vision – start-page: 929 year: 2018 end-page: 934 ident: b0225 article-title: Signed graph convolutional networks publication-title: 2018 IEEE International Conference on Data Mining (ICDM) – reference: Z. Ke, H. Vikalo, A graph auto-encoder for haplotype assembly and viral quasispecies reconstruction, in: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, 2020, pp. 719–726. – start-page: 2022 year: 2019 end-page: 2032 ident: b0400 article-title: Heterogeneous graph attention network publication-title: The World Wide Web Conference – start-page: 3894 year: 2017 end-page: 3900 ident: b0160 article-title: Fast network embedding enhancement via high order proximity approximation publication-title: IJCAI – volume: 29 start-page: 93 year: 2008 ident: b0345 article-title: Collective classification in network data publication-title: AI Magazine – reference: P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, arXiv preprint arXiv:1710.10903, 2017. – reference: J. Chen, T. Ma, C. Xiao, Fastgcn: fast learning with graph convolutional networks via importance sampling, arXiv preprint arXiv:1801.10247, 2018. – start-page: 9267 year: 2019 end-page: 9276 ident: b0320 article-title: Can gcns go as deep as cnns?, in publication-title: Proceedings of the IEEE International Conference on Computer Vision – reference: J. Scott, Social network analysis, overview of, in: Computational complexity, vols. 1–6, Springer, New York, 2012, pp. 2898–2911. – reference: F. Wu, T. Zhang, A.H. d. Souza Jr, C. Fifty, T. Yu, K.Q. Weinberger, Simplifying graph convolutional networks, arXiv preprint arXiv:1902.07153, 2019. – reference: A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, in: Advances in Neural Information Processing Systems, 2017, pp. 5998–6008. – start-page: 351 year: 1995 end-page: 358 ident: b0350 article-title: A signal processing approach to fair surface design publication-title: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques – start-page: 40 year: 2016 end-page: 48 ident: b0395 article-title: Revisiting semi-supervised learning with graph embeddings publication-title: International Conference on Machine Learning – reference: K. Xu, C. Li, Y. Tian, T. Sonobe, K.-I. Kawarabayashi, S. Jegelka, Representation learning on graphs with jumping knowledge networks, arXiv preprint arXiv:1806.03536, 2018. – volume: 85 start-page: 599 year: 1985 end-page: 618 ident: b0020 article-title: Applications of combinatorics and graph theory to spectroscopy and quantum chemistry publication-title: Chemical Reviews – start-page: 115 year: 2011 end-page: 148 ident: b0065 article-title: Node classification in social networks publication-title: Social Network Data Analytics – volume: 103 start-page: 8577 year: 2006 end-page: 8582 ident: b0145 article-title: Modularity and community structure in networks publication-title: Proceedings of the National Academy of Sciences – volume: 23 start-page: 447 year: 2011 end-page: 478 ident: b0140 article-title: Leveraging social media networks for classification publication-title: Data Mining and Knowledge Discovery – reference: T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv:1609.02907, 2016. – year: 2019 ident: b0255 article-title: T-gcn: A temporal graph convolutional network for traffic prediction publication-title: IEEE Transactions on Intelligent Transportation Systems – start-page: 855 year: 2016 end-page: 864 ident: b0100 article-title: node2vec Scalable feature learning for networks publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – start-page: 243 year: 2011 end-page: 275 ident: b0055 article-title: A survey of link prediction in social networks publication-title: Social Network Data Analytics – start-page: 889 year: 2017 end-page: 898 ident: b0285 article-title: Marginalized graph autoencoder for graph clustering publication-title: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management – reference: J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, Line: Large-scale information network embedding, in: Proceedings of the 24th international conference on world wide web, 2015, pp. 1067–1077. – year: 2018 ident: b0220 article-title: Graph neural networks: A review of methods and applications – year: 2017 ident: b0305 article-title: Representation learning on graphs: Methods and applications – start-page: 974 year: 2018 end-page: 983 ident: b0235 article-title: Graph convolutional neural networks for web-scale recommender systems publication-title: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining – start-page: 1797 year: 2017 end-page: 1806 ident: b0120 article-title: Explore meta-paths in heterogeneous information networks for representation learning publication-title: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management – start-page: 1105 year: 2016 end-page: 1114 ident: b0165 article-title: Asymmetric transitivity preserving graph embedding publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 25 start-page: 334 year: 1985 ident: 10.1016/j.neucom.2020.12.061_b0025 article-title: Applications of graph theory in chemistry publication-title: Journal of Chemical Information and Computer Sciences doi: 10.1021/ci00047a033 – start-page: 817 year: 2009 ident: 10.1016/j.neucom.2020.12.061_b0135 article-title: Relational learning via latent social dimensions – start-page: 9267 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0320 article-title: Can gcns go as deep as cnns?, in – start-page: 855 year: 2016 ident: 10.1016/j.neucom.2020.12.061_b0100 article-title: node2vec Scalable feature learning for networks – volume: 64 start-page: 6160 year: 2016 ident: 10.1016/j.neucom.2020.12.061_b0155 article-title: Learning laplacian matrix in smooth graph signal representations publication-title: IEEE Transactions on Signal Processing doi: 10.1109/TSP.2016.2602809 – start-page: 11983 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0365 article-title: Graph transformer networks – volume: 44 start-page: 150 year: 2001 ident: 10.1016/j.neucom.2020.12.061_b0010 article-title: Protein flexibility predictions using graph theory publication-title: Proteins: Structure, Function, and Bioinformatics doi: 10.1002/prot.1081 – start-page: 351 year: 1995 ident: 10.1016/j.neucom.2020.12.061_b0350 article-title: A signal processing approach to fair surface design – volume: 9 start-page: 2579 year: 2008 ident: 10.1016/j.neucom.2020.12.061_b0085 article-title: Visualizing data using t-SNE publication-title: Journal of Machine Learning Research – start-page: 1105 year: 2016 ident: 10.1016/j.neucom.2020.12.061_b0165 article-title: Asymmetric transitivity preserving graph embedding – volume: 45 start-page: 2673 year: 1997 ident: 10.1016/j.neucom.2020.12.061_b0185 article-title: Bidirectional recurrent neural networks publication-title: IEEE Transactions on Signal Processing doi: 10.1109/78.650093 – volume: 154 start-page: 923 year: 2000 ident: 10.1016/j.neucom.2020.12.061_b0030 article-title: Linkage disequilibrium, gene trees and selfing: an ancestral recombination graph with partial self-fertilization publication-title: Genetics doi: 10.1093/genetics/154.2.923 – year: 2020 ident: 10.1016/j.neucom.2020.12.061_b0380 article-title: Variational graph auto-encoders for mirna-disease association prediction publication-title: Methods – volume: 103 start-page: 8577 year: 2006 ident: 10.1016/j.neucom.2020.12.061_b0145 article-title: Modularity and community structure in networks publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.0601602103 – start-page: 974 year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0235 article-title: Graph convolutional neural networks for web-scale recommender systems – ident: 10.1016/j.neucom.2020.12.061_b0200 – volume: 33 start-page: 1548 year: 2011 ident: 10.1016/j.neucom.2020.12.061_b0150 article-title: Graph regularized nonnegative matrix factorization for data representation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2010.231 – start-page: 1165 year: 2015 ident: 10.1016/j.neucom.2020.12.061_b0110 article-title: Pte: Predictive text embedding through large-scale heterogeneous text networks – ident: 10.1016/j.neucom.2020.12.061_b0330 – volume: 486 start-page: 75 year: 2010 ident: 10.1016/j.neucom.2020.12.061_b0080 article-title: Community detection in graphs publication-title: Physics reports doi: 10.1016/j.physrep.2009.11.002 – ident: 10.1016/j.neucom.2020.12.061_b0195 – volume: 30 start-page: 1616 year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0130 article-title: A comprehensive survey of graph embedding: problems, techniques, and applications publication-title: IEEE Transactions on Knowledge and Data Engineering doi: 10.1109/TKDE.2018.2807452 – start-page: 40 year: 2016 ident: 10.1016/j.neucom.2020.12.061_b0395 article-title: Revisiting semi-supervised learning with graph embeddings – ident: 10.1016/j.neucom.2020.12.061_b0290 doi: 10.24963/ijcai.2018/362 – year: 2017 ident: 10.1016/j.neucom.2020.12.061_b0305 – ident: 10.1016/j.neucom.2020.12.061_b0180 – start-page: 459 year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0170 article-title: Network embedding as matrix factorization: unifying deepwalk, line, pte, and node2vec – ident: 10.1016/j.neucom.2020.12.061_b0050 – ident: 10.1016/j.neucom.2020.12.061_b0210 – start-page: 889 year: 2017 ident: 10.1016/j.neucom.2020.12.061_b0285 article-title: Marginalized graph autoencoder for graph clustering – volume: 24 start-page: 1093 year: 2008 ident: 10.1016/j.neucom.2020.12.061_b0015 article-title: Fitting a geometric graph to a protein–protein interaction network publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn079 – ident: 10.1016/j.neucom.2020.12.061_b0040 – volume: 2020 year: 2020 ident: 10.1016/j.neucom.2020.12.061_b0300 article-title: Semiparametric nonlinear bipartite graph representation learning with provable guarantees publication-title: International Conference on Machine Learning (ICML) – start-page: 1797 year: 2017 ident: 10.1016/j.neucom.2020.12.061_b0120 article-title: Explore meta-paths in heterogeneous information networks for representation learning – start-page: 701 year: 2014 ident: 10.1016/j.neucom.2020.12.061_b0090 article-title: Deepwalk: Online learning of social representations – start-page: 639 year: 2012 ident: 10.1016/j.neucom.2020.12.061_b0385 article-title: Deep learning via semi-supervised embedding – start-page: 3894 year: 2017 ident: 10.1016/j.neucom.2020.12.061_b0160 article-title: Fast network embedding enhancement via high order proximity approximation – ident: 10.1016/j.neucom.2020.12.061_b0240 doi: 10.1609/aaai.v32i1.11604 – volume: 20 start-page: 3940 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0355 article-title: Real-time traffic speed estimation with graph convolutional generative autoencoder publication-title: IEEE Transactions on Intelligent Transportation Systems doi: 10.1109/TITS.2019.2910560 – volume: 212 start-page: 151 year: 1990 ident: 10.1016/j.neucom.2020.12.061_b0005 article-title: Use of techniques derived from graph theory to compare secondary structure motifs in proteins publication-title: Journal of Molecular Biology doi: 10.1016/0022-2836(90)90312-A – start-page: 929 year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0225 article-title: Signed graph convolutional networks – start-page: 1416 year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0230 article-title: Large-scale learnable graph convolutional networks – ident: 10.1016/j.neucom.2020.12.061_b0190 – start-page: 6519 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0295 article-title: Symmetric graph convolutional autoencoder for unsupervised graph representation learning – start-page: 243 year: 2011 ident: 10.1016/j.neucom.2020.12.061_b0055 article-title: A survey of link prediction in social networks – ident: 10.1016/j.neucom.2020.12.061_b0325 – ident: 10.1016/j.neucom.2020.12.061_b0075 doi: 10.1609/aaai.v28i1.8916 – start-page: 1399 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0390 article-title: Robust graph convolutional networks against adversarial attacks – ident: 10.1016/j.neucom.2020.12.061_b0245 doi: 10.1609/aaai.v32i1.11691 – ident: 10.1016/j.neucom.2020.12.061_b0095 – ident: 10.1016/j.neucom.2020.12.061_b0370 – start-page: 2022 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0400 article-title: Heterogeneous graph attention network – ident: 10.1016/j.neucom.2020.12.061_b0375 doi: 10.1609/aaai.v34i01.5414 – ident: 10.1016/j.neucom.2020.12.061_b0280 – ident: 10.1016/j.neucom.2020.12.061_b0315 – start-page: 375 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0175 article-title: A general view for network embedding as matrix factorization – year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0255 article-title: T-gcn: A temporal graph convolutional network for traffic prediction publication-title: IEEE Transactions on Intelligent Transportation Systems – volume: 29 start-page: 93 year: 2008 ident: 10.1016/j.neucom.2020.12.061_b0345 article-title: Collective classification in network data publication-title: AI Magazine doi: 10.1609/aimag.v29i3.2157 – ident: 10.1016/j.neucom.2020.12.061_b0265 – start-page: 362 year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0215 article-title: Structured sequence modeling with graph convolutional recurrent networks – ident: 10.1016/j.neucom.2020.12.061_b0045 doi: 10.1007/978-1-4614-1800-9_178 – ident: 10.1016/j.neucom.2020.12.061_b0250 – volume: 1 start-page: 620 year: 2008 ident: 10.1016/j.neucom.2020.12.061_b0035 article-title: Applications of graph theory to landscape genetics publication-title: Evolutionary Applications doi: 10.1111/j.1752-4571.2008.00047.x – start-page: 115 year: 2011 ident: 10.1016/j.neucom.2020.12.061_b0065 article-title: Node classification in social networks – ident: 10.1016/j.neucom.2020.12.061_b0275 – volume: 4 start-page: 992 year: 2011 ident: 10.1016/j.neucom.2020.12.061_b0335 article-title: Meta path-based top-k similarity search in heterogeneous information networks publication-title: Proceedings of the VLDB Endowment doi: 10.14778/3402707.3402736 – volume: 85 start-page: 599 year: 1985 ident: 10.1016/j.neucom.2020.12.061_b0020 article-title: Applications of combinatorics and graph theory to spectroscopy and quantum chemistry publication-title: Chemical Reviews doi: 10.1021/cr00070a005 – year: 2018 ident: 10.1016/j.neucom.2020.12.061_b0220 – volume: 7 start-page: 1 year: 2013 ident: 10.1016/j.neucom.2020.12.061_b0340 article-title: Pathselclus: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks publication-title: ACM Transactions on Knowledge Discovery from Data (TKDD) doi: 10.1145/2500492 – start-page: 1169 year: 2011 ident: 10.1016/j.neucom.2020.12.061_b0060 article-title: Temporal link prediction by integrating content and structure information – ident: 10.1016/j.neucom.2020.12.061_b0270 doi: 10.1145/3397271.3401063 – start-page: 1 year: 2020 ident: 10.1016/j.neucom.2020.12.061_b0360 article-title: Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps publication-title: International Journal of Geographical Information Science – start-page: 85 year: 2017 ident: 10.1016/j.neucom.2020.12.061_b0125 article-title: Ice: Item concept embedding via textual information – start-page: 54 year: 2016 ident: 10.1016/j.neucom.2020.12.061_b0070 article-title: Node classification in signed social networks – ident: 10.1016/j.neucom.2020.12.061_b0105 doi: 10.1145/2736277.2741093 – volume: 23 start-page: 447 year: 2011 ident: 10.1016/j.neucom.2020.12.061_b0140 article-title: Leveraging social media networks for classification publication-title: Data Mining and Knowledge Discovery doi: 10.1007/s10618-010-0210-x – ident: 10.1016/j.neucom.2020.12.061_b0205 – start-page: 135 year: 2017 ident: 10.1016/j.neucom.2020.12.061_b0115 article-title: Scalable representation learning for heterogeneous networks – ident: 10.1016/j.neucom.2020.12.061_b0260 – start-page: 11487 year: 2019 ident: 10.1016/j.neucom.2020.12.061_b0310 article-title: Rethinking knowledge graph propagation for zero-shot learning |
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