Fetching the paper…
Reading the bibliography…
Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks.
Whitney, H.: Congruent graphs and the connectivity of graphs. American Journal of Mathematics 54
1932
Earlier work this paper cites.
Borisenko, A.I., et al.: Vector and tensor analysis with applications. Courier Corporation (1968)
1968
Earlier work this paper cites.
Berge, C.: Graphs and hypergraphs (1973)
1973
Earlier work this paper cites.
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual review of sociology 27
2001
Earlier work this paper cites.
Agarwal, S., Branson, K., Belongie, S.: Higher order learning with graphs. In: Proceedings of the 23rd international conference on Machine learning. pp. 17–24. ACM (2006)
2006
Earlier work this paper cites.
Shashua, A., Zass, R., Hazan, T.: Multi-way clustering using super-symmetric non-negative tensor factorization. In: European conference on computer vision. pp. 595–608. Springer (2006)
2006
Earlier work this paper cites.
Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: Clustering, classification, and embedding. In: Advances in neural information processing systems. pp. 1601–1608 (2007)
2007
Earlier work this paper cites.
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9
2008
Earlier work this paper cites.
Bulò, S.R., Pelillo, M.: A game-theoretic approach to hypergraph clustering. In: Advances in neural information processing systems. pp. 1571–1579 (2009)
2009
Earlier work this paper cites.
Han, Y., Zhou, B., Pei, J., Jia, Y.: Understanding importance of collaborations in co-authorship networks: A supportiveness analysis approach. In: Proceedings of the 2009 SIAM International Conference on Data Mining. pp. 1112–1123. SIAM (2009)
2009
Earlier work this paper cites.
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM review 51
2009
Earlier work this paper cites.
Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis 30
2011
Earlier work this paper cites.
Pu, L., Faltings, B.: Hypergraph learning with hyperedge expansion. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 410–425. Springer (2012)
2012
Cited alongside, same era.
Bretto, A.: Hypergraph theory. An introduction. Mathematical Engineering. Cham: Springer (2013)
2013
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
Chen, F., Gao, Y., Cao, D., Ji, R.: Multimodal hypergraph learning for microblog sentiment prediction. In: 2015 IEEE International Conference on Multimedia and Expo (ICME). pp. 1–6. IEEE (2015)
Chan, T.H.H., Louis, A., Tang, Z.G., Zhang, C.: Spectral properties of hypergraph laplacian and approximation algorithms. Journal of the ACM (JACM) 65
2018
Later among the works it cites.
Chen, J., Ma, T., Xiao, C.: FastGCN: Fast learning with graph convolutional networks via importance sampling. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=rytstxWAW
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
2016
Cited alongside, same era.
Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International conference on machine learning. pp. 2014–2023 (2016)
2016
Cited alongside, same era.
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems. pp. 1025–1035 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
2017
Cited alongside, same era.
Zhang, C., Hu, S., Tang, Z.G., Chan, T.: Re-revisiting learning on hypergraphs: confidence interval and subgradient method. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 4026–4034. JMLR. org (2017)
2017
Cited alongside, same era.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018), https://openreview.net/forum?id=rJXMpikCZ
2018
Later among the works it cites.
Chien, I.E., Zhou, H., Li, P.: Hs 2 {}^{\mbox{2}} : Active learning over hypergraphs with pointwise and pairwise queries. In: The 22nd International Conference on Artificial Intelligence and Statistics. pp. 2466–2475 (2019)
2019
Later among the works it cites.
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 3558–3565 (2019)
2019
Later among the works it cites.
Jiang, J., Wei, Y., Feng, Y., Cao, J., Gao, Y.: Dynamic hypergraph neural networks. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI). pp. 2635–2641 (2019)
2019
Later among the works it cites.
Veličković, P., Fedus, W., Hamilton, W.L., Liò, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: International Conference on Learning Representations (2019), https://openreview.net/forum?id=rklz9iAcKQ
2019
Later among the works it cites.
2019
Later among the works it cites.
Yadati, N., Nimishakavi, M., Yadav, P., Nitin, V., Louis, A., Talukdar, P.: Hypergcn: A new method for training graph convolutional networks on hypergraphs. In: Advances in Neural Information Processing Systems. pp. 1509–1520 (2019)
2019
Later among the works it cites.