Abstract
There is an increasing number of applications where data can be represented as graphs. Besides, it is well-known that artificial intelligence approaches have become a very active and promising research field, mostly due to deep learning technologies. However popular deep learning architectures were designed to treat mostly image and text data. Graph Neural Network is the branch of machine learning which builds neural networks for graph data. In this context, many authors have recently proposed to adapt existing approaches to graphs and networks. In this paper we train three models of Graph Neural Networks on an academic citation network of Computer Science papers, and we explore the advantages of turning the problem into a multilabel classification problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The neighborhood can include the node itself.
- 2.
There are 10 papers whose publication date is before 1991, which is the year arXiv was publicly released.
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015 Conference Track Proceedings (2015)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3844–3852. NIPS 2016, Curran Associates Inc., Red Hook, NY, USA (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2–7 June 2019, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423
Dwivedi, V.P., Joshi, C.K., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv:2003.00982 [cs, stat] (2020)
Fey, M., Lenssen, J.E.: Fast graph representation learning with pytorch geometric. arXiv:1903.02428 [cs, stat] (2019)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 1263–1272. PMLR (2017)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035. NIPS 2017, Curran Associates Inc., Red Hook, NY, USA (2017)
Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmonic Anal. 30(2), 129–150 (2011). https://doi.org/10.1016/j.acha.2010.04.005
Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H.T. (eds.) Advances in Neural Information Processing Systems, vol. 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6–12 December 2020, Virtual (2020)
Huang, Q., He, H., Singh, A., Lim, S.N., Benson, A.R.: Combining label propagation and simple models out-performs graph neural networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3–7 May 2021. OpenReview.net (2021)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings. OpenReview.net (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Lanchantin, J., Sekhon, A., Qi, Y.: Neural message passing for multi-label classification. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 138–163. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46147-8_9
Li, G., Muller, M., Thabet, A., Ghanem, B.: DeepGCNs: can GCNs go as deep as CNNs? In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9266–9275. IEEE, Seoul, Korea (South) (2019). https://doi.org/10.1109/ICCV.2019.00936
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Bengio, Y., LeCun, Y. (eds.) 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, 2–4 May 2013, Workshop Track Proceedings (2013)
Nam, J., Kim, J., Loza Mencía, E., Gurevych, I., Fürnkranz, J.: Large-scale multi-label text classification — revisiting neural networks. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 437–452. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44851-9_28
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008). https://doi.org/10.1609/aimag.v29i3.2157
Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Trans. Neural Netw. 8(3), 714–735 (1997). https://doi.org/10.1109/72.572108
Sun, C., Wu, G.: Adaptive graph diffusion networks with hop-wise attention. arXiv:2012.15024 [cs] (2020)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April –3 May 2018, Conference Track Proceedings. OpenReview.net (2018)
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: a unified framework for multi-label image classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2285–2294. IEEE, Las Vegas, NV, USA (2016). https://doi.org/10.1109/CVPR.2016.251
Wang, K., Shen, Z., Huang, C., Wu, C.H., Dong, Y., Kanakia, A.: Microsoft academic graph: when experts are not enough. Quant. Sci. Stud. 1(1), 396–413 (2020). https://doi.org/10.1162/qss_a_00021
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2021). https://doi.org/10.1109/TNNLS.2020.2978386
Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: a survey. IEEE Trans. Knowl. Data Eng. 34(1), 249–270 (2022). https://doi.org/10.1109/TKDE.2020.2981333
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lachaud, G., Conde-Cespedes, P., Trocan, M. (2022). Graph Neural Networks-Based Multilabel Classification of Citation Network. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-21967-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21966-5
Online ISBN: 978-3-031-21967-2
eBook Packages: Computer ScienceComputer Science (R0)