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Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph.
Graph neural networks with convolutional arma filters
Bianchi, F. M., Grattarola, D., Livi, L., and Alippi, C · 1901
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Hierarchical representation learning in graph neural networks with node decimation pooling
Bianchi, F. M., Grattarola, D., Livi, L., and Alippi, C · 1910
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The monotonicity theorem, cauchy’s interlace theorem, and the courant-fischer theorem
Ikebe, Y., Inagaki, T., and Miyamoto, S · 1987
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Normalized cuts and image segmentation
Shi, J. and Malik, J · 2000
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Regions adjacency graph applied to color image segmentation
Trémeau, A. and Colantoni, P · 2000
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Birds of a feather: Homophily in social networks
McPherson, M., Smith-Lovin, L., and Cook, J. M · 2001
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Mixing patterns in networks
Newman, M. E · 2003
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Multiclass spectral clustering
Yu and Shi · 2003
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Kernel k-means: spectral clustering and normalized cuts
Dhillon, I. S., Guan, Y., and Kulis, B · 2004
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Efficient graph-based image segmentation
Felzenszwalb, P. F. and Huttenlocher, D. P · 2004
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Weighted graph cuts without eigenvectors a multilevel approach
Dhillon, I. S., Guan, Y., and Kulis, B · 2007
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A tutorial on spectral clustering
Von Luxburg, U · 2007
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., Leeuwen, E. J. v., Mehlhorn, K., and Borgwardt, K. M · 2011
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2013
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A feasible method for optimization with orthogonality constraints
Wen, Z. and Yin, W · 2013
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Spectral clustering with a convex regularizer on millions of images
Collins, M. D., Liu, J., Xu, J., Mukherjee, L., and Singh, V · 2014
Cited alongside, same era.
Learning deep representations for graph clustering
Tian, F., Gao, B., Cui, Q., Chen, E., and Liu, T.-Y · 2014
Cited alongside, same era.
Deep spectral clustering learning
Law, M. T., Urtasun, R., and Zemel, R. S · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., and Bronstein, M. M · 2017
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Dynamic edgeconditioned filters in convolutional neural networks on graphs
Simonovsky, M. and Komodakis, N · 2017
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Syncspeccnn: Synchronized spectral cnn for 3d shape segmentation
Yi, L., Su, H., Guo, X., and Guibas, L. J · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Damle, A., Minden, V., and Ying, L · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2016
Cited alongside, same era.
A multiscale pyramid transform for graph signals
Shuman, D. I., Faraji, M. J., and Vandergheynst, P · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Mini-batch spectral clustering
Han, Y. and Filippone, M · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Cited alongside, same era.
Cangea, C., Veličković, P., Jovanović, N., Kipf, T., and Liò, P · 2018
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Fey, M., Lenssen, J. E., Weichert, F., and Müller, H · 2018
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Spectralnet: Spectral clustering using deep neural networks
Shaham, U., Stanton, K., Li, H., Nadler, B., Basri, R., and Kluger, Y · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Graph u-nets
Hongyang Gao, S. J · 2019
Closest in time.
Self-attention graph pooling
Lee, J., Lee, I., and Kang, J · 2019
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Fast and deep graph neural networks
Gallicchio, C. and Micheli, A · 2020
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