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Graph Neural Networks (GNNs) have shown remarkable success in learning from graph-structured data.
Spectral graph theory , volume 92
Chung, F. R · 1997
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The pagerank citation ranking: Bringing order to the web
Page, L., Brin, S., Motwani, R., and Winograd, T · 1999
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Beyond answers: Dimensions of the advice network
Cross, R., Borgatti, S. P., and Parker, A · 2001
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Reversible markov chains and random walks on graphs, 2002
Aldous, D. and Fill, J · 2002
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Directed graph convolutional network
Tong, Z., Liang, Y., Sun, C., Rosenblum, D. S., and Lim, A · 2004
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Laplacians and the cheeger inequality for directed graphs
Chung, F · 2005
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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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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
Halko, N., Martinsson, P.-G., and Tropp, J. A · 2011
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Matrix analysis
Horn, R. A. and Johnson, C. R · 2012
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Digraph laplacian and the degree of asymmetry
Li, Y. and Zhang, Z.-L · 2012
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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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Graph fourier transform based on directed laplacian
Singh, R., Chakraborty, A., and Manoj, B · 2016
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Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking
Bojchevski, A. and Günnemann, S · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Graph Attention Networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Ver Steeg, G., and Galstyan, A · 2019
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2019
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Directed graph contrastive learning
Tong, Z., Liang, Y., Ding, H., Dai, Y., Li, X., and Wang, C · 2021
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Magnet: A neural network for directed graphs
Zhang, X., He, Y., Brugnone, N., Perlmutter, M., and Hirn, M · 2021
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DiffWire: Inductive Graph Rewiring via the Lovász Bound
Arnaiz-Rodríguez, A., Begga, A., Escolano, F., and Oliver, N. M · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J., Giovanni, F. D., Chamberlain, B. P., Dong, X., and Bronstein, M. M · 2022
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Lightgcl: Simple yet effective graph contrastive learning for recommendation
Cai, X., Huang, C., Xia, L., and Ren, X · 2023
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
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Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2019
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Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
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Simple and deep graph convolutional networks
Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y · 2020
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Graph signal processing for directed graphs based on the hermitian laplacian
Furutani, S., Shibahara, T., Akiyama, M., Hato, K., and Aida, M · 2020
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Graph representation learning
Hamilton, W. L · 2020
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Gag: Global attributed graph neural network for streaming session-based recommendation
Qiu, R., Yin, H., Huang, Z., and Chen, T · 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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Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Lio, P., and Bronstein, M. M · 2023
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Sigmanet: One laplacian to rule them all
Fiorini, S., Coniglio, S., Ciavotta, M., and Messina, E · 2023
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Transformers meet directed graphs
Geisler, S., Li, Y., Mankowitz, D. J., Cemgil, A. T., Günnemann, S., and Paduraru, C · 2023
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A magnetic framelet-based convolutional neural network for directed graphs
Lin, L. and Gao, J · 2023
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Edge directionality improves learning on heterophilic graphs
Rossi, E., Charpentier, B., Giovanni, F. D., Frasca, F., Günnemann, S., and Bronstein, M. M · 2023
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Delaunay graph: Addressing over-squashing and over-smoothing using delaunay triangulation
Attali, H., Buscaldi, D., and Pernelle, N · 2024
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DUPLEX: Dual GAT for complex embedding of directed graphs
Ke, Z., Yu, H., Li, J., and Zhang, H · 2024
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Holonets: Spectral convolutions do extend to directed graphs
Koke, C. and Cremers, D · 2024
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Transformers over directed acyclic graphs
Luo, Y., Thost, V., and Shi, L · 2024
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GNNs getting comfy: Community and feature similarity guided rewiring
Rubio-Madrigal, C., Jamadandi, A., and Burkholz, R · 2025
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