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As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community.
Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Kabsch, W. and Sander, C · 1983
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Topics in matrix analysis, 1986
Horn, R. A · 1986
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The laplacian of a hypergraph
Chung, F · 1993
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Scop: a structural classification of proteins database for the investigation of sequences and structures
Murzin, A. G., Brenner, S. E., Hubbard, T., and Chothia, C · 1995
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Spectral graph theory
Chung, F. R. and Graham, F. C · 1997
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Learning conformation rules
Maruyama, O., Shoudai, T., Furuichi, E., Kuhara, S., and Miyano, S · 2001
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On visual similarity based 3d model retrieval
Chen, D.-Y., Tian, X.-P., Shen, Y.-T., and Ouhyoung, M · 2003
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Lga: a method for finding 3d similarities in protein structures
Zemla, A · 2003
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Higher order learning with graphs
Agarwal, S., Branson, K., and Belongie, S · 2006
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Learning with hypergraphs: Clustering, classification, and embedding
Zhou, D., Huang, J., and Schölkopf, B · 2006
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Interactive image segmentation using probabilistic hypergraphs
Ding, L. and Yilmaz, A · 2010
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Image retrieval via probabilistic hypergraph ranking
Huang, Y., Liu, Q., Zhang, S., and Metaxas, D. N · 2010
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A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction
Zhang, J. and Zhang, Y · 2010
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Wavelets on graphs via spectral graph theory
Hammond, D. K., Vandergheynst, P., and Gribonval, R · 2011
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High-ordered random walks and generalized laplacians on hypergraphs
Lu, L. and Peng, X · 2011
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Random walks in hypergraph
Bellaachia, A. and Al-Dhelaan, M · 2013
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The total variation on hypergraphs-learning on hypergraphs revisited
Hein, M., Setzer, S., Jost, L., and Rangapuram, S. S · 2013
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lddt: a local superposition-free score for comparing protein structures and models using distance difference tests
Mariani, V., Biasini, M., Barbato, A., and Schwede, T · 2013
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P · 2013
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Random walks in directed hypergraphs and application to semi-supervised image segmentation
Ducournau, A. and Bretto, A · 2014
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The network data repository with interactive graph analytics and visualization
Rossi, R. and Ahmed, N · 2015
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Multi-view convolutional neural networks for 3d shape recognition
Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E · 2015
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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Higher-order organization of complex networks
Benson, A. R., Gleich, D. F., and Leskovec, J · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Learn to rank images: A unified probabilistic hypergraph model for visual search
Zeng, K., Wu, N., Sargolzaei, A., and Yen, K · 2016
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The spacey random walk: A stochastic process for higher-order data
Benson, A. R., Gleich, D. F., and Lim, L.-H · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inhomogeneous hypergraph clustering with applications
Li, P. and Milenkovic, O · 2017
Cited alongside, same era.
Random walks and diffusion on networks
Masuda, N., Porter, M. A., and Lambiotte, R · 2017
Cited alongside, same era.
Voromqa: Assessment of protein structure quality using interatomic contact areas
Olechnovivc, K. and Venclovas, v · 2017
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Re-revisiting learning on hypergraphs: confidence interval and subgradient method
Zhang, C., Hu, S., Tang, Z. G., and Chan, T. H · 2017
Cited alongside, same era.
Learning protein sequence embeddings using information from structure
Hypernetwork science via high-order hypergraph walks
Aksoy, S. G., Joslyn, C., Marrero, C. O., Praggastis, B., and Purvine, E · 2020
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Hypersage: Generalizing inductive representation learning on hypergraphs
Arya, D., Gupta, D. K., Rudinac, S., and Worring, M · 2020
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Graphqa: Protein model quality assessment using graph convolutional networks
Baldassarre, F., Hurtado, D., Elofsson, A., and Azizpour, H · 2020
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Random walks on hypergraphs
Carletti, T., Battiston, F., Cencetti, G., and Fanelli, D · 2020
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Generalizing the hypergraph laplacian via a diffusion process with mediators
Chan, T.-H. H. and Liang, Z · 2020
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Simple and deep graph convolutional networks
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Bepler, T. and Berger, B · 2018
Cited alongside, same era.
Deep convolutional networks for quality assessment of protein folds
Derevyanko, G., Grudinin, S., Bengio, Y., and Lamoureux, G · 2018
Cited alongside, same era.
Gvcnn: Group-view convolutional neural networks for 3d shape recognition
Feng, Y., Zhang, Z., Zhao, X., Ji, R., and Gao, Y · 2018
Cited alongside, same era.
Deepsf: deep convolutional neural network for mapping protein sequences to folds
Hou, J., Adhikari, B., and Cheng, J · 2018
Cited alongside, same era.
Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2018
Cited alongside, same era.
Motifnet: a motif-based graph convolutional network for directed graphs
Monti, F., Otness, K., and Bronstein, M. M · 2018
Cited alongside, same era.
Graph attention networks
Velivcković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Cited alongside, same era.
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
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Be more with less: Hypergraph attention networks for inductive text classification
Ding, K., Wang, J., Li, J., Li, D., and Liu, H · 2020
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Hnhn: Hypergraph networks with hyperedge neurons
Dong, Y., Sawin, W., and Bengio, Y · 2020
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Structure-based function prediction using graph convolutional networks
Gligorijevic, V., Renfrew, P. D., Kosciolek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I. M., Vlamakis, H., et al · 2020
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Hypergraph random walks, laplacians, and clustering
Hayashi, K., Aksoy, S. G., Park, C. H., and Park, H · 2020
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Scalable graph convolutional networks with fast localized spectral filter for directed graphs
Li, C., Qin, X., Xu, X., Yang, D., and Wei, G · 2020
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Udsmprot: universal deep sequence models for protein classification
Strodthoff, N., Wagner, P., Wenzel, M., and Samek, W · 2020
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Directed graph convolutional network
Tong, Z., Liang, Y., Sun, C., Rosenblum, D. S., and Lim, A · 2020
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Powerset convolutional neural networks
Wendler, C., Püschel, M., and Alistarh, D · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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Neural message passing for multi-relational ordered and recursive hypergraphs
Yadati, N · 2020
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Hypergraph label propagation network
Zhang, Y., Wang, N., Chen, Y., Zou, C., Wan, H., Zhao, X., and Gao, Y · 2020
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Pairnorm: Tackling oversmoothing in {gnn}s
Zhao, L. and Akoglu, L · 2020
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Random walks and community detection in hypergraphs
Carletti, T., Fanelli, D., and Lambiotte, R · 2021
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Diversified multiscale graph learning with graph self-correction
Chen, Y., Bian, Y., Zhang, J., Xiao, X., Xu, T., Rong, Y., and Huang, J · 2021
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You are allset: A multiset function framework for hypergraph neural networks, 2021
Chien, E., Pan, C., Peng, J., and Milenkovic, O · 2021
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p p -laplacian based graph neural networks
Fu, G., Zhao, P., and Bian, Y · 2021
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Intrinsic-extrinsic convolution and pooling for learning on 3d protein structures
Hermosilla, P., Schäfer, M., Lang, M., Fackelmann, G., Vázquez, P. P., Kozlíková, B., Krone, M., Ritschel, T., and Ropinski, Timo, M · 2021
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Unignn: a unified framework for graph and hypergraph neural networks
Huang, J. and Yang, J · 2021
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Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds
Xu, M., Ding, R., Zhao, H., and Qi, X · 2021
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Simple spectral graph convolution
Zhu, H. and Koniusz, P · 2021
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Preventing over-smoothing for hypergraph neural networks
Chen, G. and Zhang, J · 2022
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Learnable hypergraph laplacian for hypergraph learning
Zhang, J., Chen, Y., Xiao, X., Lu, R., and Xia, S.-T · 2022
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