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Benchmark datasets have proved pivotal to the success of graph learning, and good benchmark datasets are crucial to guide the development of the field.
Are powerful graph neural nets necessary? A dissection on graph classification, 2020
Chen, T., Bian, S., and Sun, Y · 1905
Earlier work this paper cites.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., and Hansch, C · 1991
Earlier work this paper cites.
Resistance distance
Randić, M. and Klein, D · 1993
Earlier work this paper cites.
Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D. and Doig, A. J · 2003
Earlier work this paper cites.
Heat kernels, manifolds and graph embedding
Bai, X. and Hancock, E. R · 2004
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AIDS antiviral screen data, 2004
NIH National Cancer Institute · 2004
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BRENDA, the enzyme database: updates and major new developments
Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G., and Schomburg, D · 2004
Earlier work this paper cites.
Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S. V. N., Smola, A. J., and Kriegel, H.-P · 2005
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Graphs over time: densification laws, shrinking diameters and possible explanations
Leskovec, J., Kleinberg, J., and Faloutsos, C · 2005
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Diffusion maps
Coifman, R. R. and Lafon, S · 2006
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Comparison of descriptor spaces for chemical compound retrieval and classification
Wale, N. and Karypis, G · 2006
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IAM graph database repository for graph based pattern recognition and machine learning
Riesen, K. and Bunke, H · 2008
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A survey of graph edit distance
Gao, X., Xiao, B., Tao, D., and Li, X · 2010
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Weisfeiler-lehman graph kernels
Shervashidze, N., Schweitzer, P., van Leeuwen, E. J., Mehlhorn, K., and Borgwardt, K. M · 2011
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A pipeline for fair comparison of graph neural networks in node classification tasks, 2020
Zhao, W., Zhou, D., Qiu, X., and Jiang, W · 2012
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Zinc 15 – ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
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Deep graph kernels
Yanardag, P. and Vishwanathan, S · 2015
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Network science
Barabási, A.-L · 2016
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Benchmark data sets for graph kernels, 2016
Kersting, K., Kriege, N. M., Morris, C., Mutzel, P., and Neumann, M · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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A simple yet effective baseline for non-attributed graph classification
Cai, C. and Wang, Y · 2018
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An approach for validating quality of datasets for machine learning
Ding, J. and Li, X · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Earlier work this paper cites.
Networks
Newman, M · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
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Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 2019
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How powerful are graph neural networks?, 2019
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Pitfalls in machine learning research: Reexamining the development cycle
Biderman, S. and Scheirer, W. J · 2020
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Graph kernels: State-of-the-art and future challenges
Borgwardt, K., Ghisu, E., Llinares-López, F., O’Bray, L., and Rieck, B · 2020
Cited alongside, same era.
A fair comparison of graph neural networks for graph classification
Errica, F., Podda, M., Bacciu, D., and Micheli, A · 2020
Cited alongside, same era.
Structured self-attention architecture for graph-level representation learning
Fan, X., Gong, M., Xie, Y., Jiang, F., and Li, H · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs, 2020
Task-agnostic graph explanations
Xie, Y., Katariya, S., Tang, X., Huang, E., Rao, N., Subbian, K., and Ji, S · 2022
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A new perspective on the effects of spectrum in graph neural networks
Yang, M., Shen, Y., Li, R., Qi, H., Zhang, Q., and Yin, B · 2022
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Evaluating explainability for graph neural networks
Agarwal, C., Queen, O., Lakkaraju, H., and Zitnik, M · 2023
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Graphtester: Exploring theoretical boundaries of gnns on graph datasets
Akbiyik, E., Grötschla, F., Egressy, B., and Wattenhofer, R · 2023
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A demonstration of interpretability methods for Graph Neural Networks
Bonabi Mobaraki, E. and Khan, A · 2023
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Lio, P., and Bronstein, M. M · 2023
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Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
Cited alongside, same era.
A survey on graph kernels
Kriege, N. M., Johansson, F. D., and Morris, C · 2020
Cited alongside, same era.
Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
Cited alongside, same era.
Tree! I am no tree! I am a low dimensional hyperbolic embedding
Sonthalia, R. and Gilbert, A · 2020
Cited alongside, same era.
A deep learning approach to antibiotic discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al · 2020
Cited alongside, same era.
Graph random neural features for distance-preserving graph representations
Zambon, D., Alippi, C., and Livi, L · 2020
Cited alongside, same era.
On the bottleneck of Graph Neural Networks and its practical implications
Alon, U. and Yahav, E · 2021
Cited alongside, same era.
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Towards understanding and reducing graph structural noise for gnns
Dong, M. and Kluger, Y · 2023
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2023
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Germani, E., Fromont, E., Maurel, P., and Maumet, C · 2023
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Structural Fairness-aware Active Learning for Graph Neural Networks
Han, H., Liu, X., Ma, L., Torkamani, M., Liu, H., Tang, J., and Yamada, M · 2023
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Graph matching with bi-level noisy correspondence
Lin, Y., Yang, M., Yu, J., Hu, P., Zhang, C., and Peng, X · 2023
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When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability
Luan, S., Hua, C., Xu, M., Lu, Q., Zhu, J., Chang, X., Fu, J., Leskovec, J., and Precup, D · 2023
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Demystifying structural disparity in graph neural networks: Can one size fit all?
Mao, H., Chen, Z., Jin, W., Han, H., Ma, Y., Zhao, T., Shah, N., and Tang, J · 2023
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DataPerf: Benchmarks for data-centric AI development
Mazumder, M., Banbury, C., Yao, X., Karlaš, B., Gaviria Rojas, W., Diamos, S., Diamos, G., He, L., Parrish, A., Kirk, H. R., Quaye, J., Rastogi, C., Kiela, D., Jurado, D., Kanter, D., Mosquera, R., Cukierski, W., Ciro, J., Aroyo, L., Acun, B., Chen, L., Raje, M., Bartolo, M., Eyuboglu, E. S., Ghorbani, A., Goodman, E., Howard, A., Inel, O., Kane, T., Kirkpatrick, C. R., Sculley, D., Kuo, T.-S., Mueller, J. W., Thrush, T., Vanschoren, J., Warren, M., Williams, A., Yeung, S., Ardalani, N., Paritosh, P., Zhang, C., Zou, J. Y., Wu, C.-J., Coleman, C., Ng, A., Mattson, P., and Janapa Reddi, V · 2023
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Path Neural Networks: Expressive and accurate graph neural networks
Michel, G., Nikolentzos, G., Lutzeyer, J., and Vazirgiannis, M · 2023
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Normed spaces for graph embedding
Taha, D., Zhao, W., Riestenberg, J. M., and Strube, M · 2023
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Robust attributed graph alignment via joint structure learning and optimal transport
Tang, J., Zhang, W., Li, J., Zhao, K., Tsung, F., and Li, J · 2023
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Where did the gap go? reassessing the long-range graph benchmark
Tönshoff, J., Ritzert, M., Rosenbluth, E., and Grohe, M · 2023
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Structural explanations for Graph Neural Networks using HSIC, February 2023
Toyokuni, A. and Yamada, M · 2023
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GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks, 2023
Wang, X. and Shen, H.-W · 2023
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Graph neural networks use graphs when they shouldn’t
Bechler-Speicher, M., Amos, I., Gilad-Bachrach, R., and Globerson, A · 2024
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Rethinking the effectiveness of graph classification datasets in benchmarks for assessing gnns
Li, Z., Cao, Y., Shuai, K., Miao, Y., and Hwang, K · 2024
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Metric space magnitude for evaluating the diversity of latent representations
Limbeck, K., Andreeva, R., Sarkar, R., and Rieck, B · 2024
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Position: Future directions in the theory of graph machine learning
Morris, C., Frasca, F., Dym, N., Maron, H., Ceylan, I. I., Levie, R., Lim, D., Bronstein, M. M., Grohe, M., and Jegelka, S · 2024
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A survey of graph neural networks for social recommender systems
Sharma, K., Lee, Y.-C., Nambi, S., Salian, A., Shah, S., Kim, S.-W., and Kumar, S · 2024
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One model many scores: Using multiverse analysis to prevent fairness hacking and evaluate the influence of model design decisions
Simson, J., Pfisterer, F., and Kern, C · 2024
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Mapping the multiverse of latent representations
Wayland, J., Coupette, C., and Rieck, B · 2024
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