[1] S. Agarwal, K. Branson, and S. Belongie, Higher order learning with graphs, In Proceedings of the 23rd International Conference on Machine Learning, ACM (2006) 17–24.
[2] K. Benzi, V. Kalofolias, X. Bresson, and P. Vandergheynst, Song recommendation with non-negative matrix factorization and graph total variation, In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, (2016) 2439–2443.
[3] C. Berge, Graphs and Hypergraphs, North-Holland mathematical library, Elsevier, 6 1973.
[4] M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, Geometric deep learning: going beyond euclidean data, IEEE Signal Processing Magazine, 34 (2017) 18–42.
[5] J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, Spectral networks and locally connected networks on graphs, International Conference on Learning Representations (ICLR2014), CBLS, (2014).
[6] J. Bu, S. Tan, C. Chen, C. Wang, H. Wu, L. Zhang, and X. He, Music recommendation by unified hypergraph: combining social media information and music content, Proceedings of the 18th ACM international conference on Multimedia, ACM, (2010) 391–400.
[7] E. J. Candes and Y. Plan, Matrix completion with noise, Proc. IEEE, 98 (2010) 925–936.
[8] J. Chen, H. Gao, Z. Wu, and D. Li, Tag co-occurrence relationship prediction in heterogeneous information networks, Parallel and Distributed Systems (ICPADS), International Conference on IEEE, (2013) 528–533.
[9] M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, Advances in Neural Information Processing Systems, (2016) 3844–3852.
[10] D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, Convolutional networks on graphs for learning molecular fingerprints, Advances in Neural Information Processing Systems, (2015) 2224–2232.
[11] M. Gori, G. Monfardini, and F. Scarselli, A new model for learning in graph domains, IJCNN’05, 2 (2005) 729–734.
[12] M. Henaff, J. Bruna, and Y. LeCun, Deep convolutional networks on graphstructured data, (2015) Preprint at arXiv:1506.05163.
[13] J. Johnson, L. Ballan, and L. Fei-Fei, Love thy neighbors: Image annotation by exploiting image metadata, Proceedings of the IEEE International Conference on Computer Vision, (2015) 4624–4632.
[14] V. Kalofolias, X. Bresson, M. Bronstein, and P. Vandergheynst, Matrix completion on graphs, (2014), arXiv:1408.1717.
[15] T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, (2016), arXiv:1609.02907
[16] A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, (2012) 1097–1105.
[17] D. Li, Z. Xu, S. Li, and X. Sun, Link prediction in social networks based on hypergraph, Proceedings of the 22nd International Conference on World Wide Web, ACM, (2013) 41–42.
[18] D. Liu, N. Blenn, and P. Van Mieghem, A social network model exhibiting tunable overlapping community structure, Procedia Computer Science, 9 (2012) 1400–1409.
[19] Y.-F. Liu, J.-M. Guo, and An. Lingling, Multimedia Classification Using Bipolar Relation Graphs, IEEE Transactions on Multimedia, 19 (2017) 1860–1869.
[20] J. McAuley and J. Leskovec, Image labeling on a network: using socialnetwork metadata for image classification, Computer Vision–ECCV, 2012 (2012) 828–841.
[21] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, Measurement and analysis of online social networks, Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, ACM (2007) 29–42.
[22] F. Monti, M. Bronstein, and X. Bresson, Geometric matrix completion with recurrent multi-graph neural networks, Advances in Neural Information Processing Systems, (2017) 3700–3710.
[23] A. A. Moreira, D. R. Paula, R. N. Costa Filho, and J. S. Jr. Andrade, Competitive cluster growth in complex networks, Physical Review E, 73 (2006) 065101.
[24] Z. Niu, G. Hua, X. Gao, and Q. Tian, Semi-supervised relational topic model for weakly annotated image recognition in social media, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2014) 4233–4240.
[25] N. Rao, H. F. Yu, P. K. Ravikumar and, I. S. Dhillon, Collaborative filtering with graph information: Consistency and scalable methods, Advances in neural information processing systems, (2015) 2107–2115.
[26] E. Ravasz and Barabási, A.L., Hierarchical organization in complex networks, Physical Review E 67(2) (2003) 026112.
[27] L. Sun, S. Ji, and J. Ye, Hypergraph spectral learning for multi-label classification, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2008) 668–676.
[28] M. M. Wolf, A. M. Klinvex and D. M. Dunlavy, Advantages to modeling relational data using hypergraphs versus graphs, HPEC, IEEE (2016) 1–7.
[29] D. Zhou, J. Huang, and B. Schölkopf, Learning with hypergraphs: Clustering, classification and embedding, Advances in Neural Information Processing Systems, (2007) 1601–1608.