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Graph Neural Networks (GNNs) have shown exceptional performance in the task of link prediction.
Emergence of scaling in random networks
Barabási, A.-L. and Albert, R · 1999
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Simrank: a measure of structural-context similarity
Jeh, G. and Widom, J · 2002
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Friends and neighbors on the web
Adamic, L. A. and Adar, E · 2003
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Agl: a scalable system for industrial-purpose graph machine learning
Zhang, D., Huang, X., Liu, Z., Hu, Z., Song, X., Ge, Z., Zhang, Z., Wang, L., Zhou, J., Shuang, Y., et al · 2003
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Link prediction approach to collaborative filtering
Huang, Z., Li, X., and Chen, H · 2005
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Sampling from large graphs
Leskovec, J. and Faloutsos, C · 2006
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Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., and Li, H · 2007
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Matrix factorization techniques for recommender systems
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Link mining: Models, algorithms, and applications
Philip, S. Y., Han, J., and Faloutsos, C · 2010
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Iterative graph self-distillation
Zhang, H., Lin, S., Liu, W., Zhou, P., Tang, J., Liang, X., and Xing, E. P · 2010
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Reprint of: The anatomy of a large-scale hypertextual web search engine
Brin, S. and Page, L · 2012
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., and Bengio, Y · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J., et al · 2015
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Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
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Deep neural networks for youtube recommendations
Covington, P., Adams, J., and Sargin, E · 2016
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A survey of link prediction in complex networks
Martínez, V., Berzal, F., and Cubero, J.-C · 2016
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Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., and Bouchard, G · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
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Graph convolutional matrix completion
Berg, R. v. d., Kipf, T. N., and Welling, M · 2017
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Bojchevski, A. and Günnemann, S · 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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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Weisfeiler-lehman neural machine for link prediction
Zhang, M. and Chen, Y · 2017
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Hyperspherical variational auto-encoders
Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., and Tomczak, J. M · 2018
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Born again neural networks
Furlanello, T., Lipton, Z., Tschannen, M., Itti, L., and Anandkumar, A · 2018
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Paraphrasing complex network: Network compression via factor transfer
Kim, J., Park, S., and Kwak, N · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. v. d., Titov, I., and Welling, M · 2018
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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Verse: Versatile graph embeddings from similarity measures
Tsitsulin, A., Mottin, D., Karras, P., and Müller, E · 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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Graphrnn: Generating realistic graphs with deep auto-regressive models
You, J., Ying, R., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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An end-to-end deep learning architecture for graph classification
Zhang, M., Cui, Z., Neumann, M., and Chen, Y · 2018
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Hyperbolic graph convolutional neural networks
Chami, I., Ying, Z., Ré, C., and Leskovec, J · 2019
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Graph-free knowledge distillation for graph neural networks
Deng, X. and Zhang, Z · 2021
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Ding, H., Ma, Y., Deoras, A., Wang, Y., and Wang, H · 2021
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Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
Fey, M., Lenssen, J. E., Weichert, F., and Leskovec, J · 2021
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A survey of quantization methods for efficient neural network inference
Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M. W., and Keutzer, K · 2021
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Knowledge distillation: A survey
Gou, J., Yu, B., Maybank, S. J., and Tao, D · 2021
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Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 2019
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From zero-shot learning to cold-start recommendation
Li, J., Jing, M., Lu, K., Zhu, L., Yang, Y., and Huang, Z · 2019
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Graph representation learning via multi-task knowledge distillation
Ma, J. and Mei, Q · 2019
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Provably powerful graph networks
Maron, H., Ben-Hamu, H., Serviansky, H., and Lipman, Y · 2019
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Learning attention-based embeddings for relation prediction in knowledge graphs
Nathani, D., Chauhan, J., Sharma, C., and Kaul, M · 2019
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Relational knowledge distillation
Park, W., Kim, D., Lu, Y., and Cho, M · 2019
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Few-shot graph learning for molecular property prediction
Guo, Z., Zhang, C., Yu, W., Herr, J., Wiest, O., Jiang, M., and Chawla, N. V · 2021
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Graph-mlp: node classification without message passing in graph
Hu, Y., You, H., Wang, Z., Wang, Z., Zhou, E., and Gao, Y · 2021
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On representation knowledge distillation for graph neural networks
Joshi, C. K., Liu, F., Xun, X., Lin, J., and Foo, C.-S · 2021
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Topology distillation for recommender system
Kang, S., Hwang, J., Kweon, W., and Yu, H · 2021
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Elastic graph neural networks
Liu, X., Jin, W., Ma, Y., Li, Y., Liu, H., Wang, Y., Yan, M., and Tang, J · 2021
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A unified view on graph neural networks as graph signal denoising
Ma, Y., Liu, X., Zhao, T., Liu, Y., Tang, J., and Shah, N · 2021
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Rankdistil: Knowledge distillation for ranking
Reddi, S., Pasumarthi, R. K., Menon, A., Rawat, A. S., Yu, F., Kim, S., Veit, A., and Kumar, S · 2021
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Graph neural networks for friend ranking in large-scale social platforms
Sankar, A., Liu, Y., Yu, J., and Shah, N · 2021
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Adversarially generating rank-constrained graphs
Shiao, W. and Papalexakis, E. E · 2021
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Pairwise learning for neural link prediction
Wang, Z., Zhou, Y., Hong, L., Zou, Y., and Su, H · 2021
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Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework
Yang, C., Liu, J., and Shi, C · 2021
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Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction
Yun, S., Kim, S., Lee, J., Kang, J., and Kim, H. J · 2021
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Cold brew: Distilling graph node representations with incomplete or missing neighborhoods
Zheng, W., Huang, E. W., Rao, N., Katariya, S., Wang, Z., and Subbian, K · 2021
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Accelerating large scale real-time gnn inference using channel pruning
Zhou, H., Srivastava, A., Zeng, H., Kannan, R., and Prasanna, V · 2021
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Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhu, Z., Zhang, Z., Xhonneux, L.-P., and Tang, J · 2021
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Graph trend filtering networks for recommendation
Fan, W., Liu, X., Jin, W., Zhao, X., Tang, J., and Li, Q · 2022
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Expressiveness and approximation properties of graph neural networks
Geerts, F. and Reutter, J. L · 2022
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Boosting graph neural networks via adaptive knowledge distillation
Guo, Z., Zhang, C., Fan, Y., Tian, Y., Zhang, C., and Chawla, N · 2022
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Graph rationalization with environment-based augmentations
Liu, G., Zhao, T., Xu, J., Luo, T., and Jiang, M · 2022
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Friend story ranking with edge-contextual local graph convolutions
Tang, X., Liu, Y., He, X., Wang, S., and Shah, N · 2022
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Nosmog: Learning noise-robust and structure-aware mlps on graphs
Tian, Y., Zhang, C., Guo, Z., Zhang, X., and Chawla, N. V · 2022
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Algorithm and system co-design for efficient subgraph-based graph representation learning
Yin, H., Zhang, M., Wang, Y., Wang, J., and Li, P · 2022
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Multi-task self-supervised graph neural networks enable stronger task generalization
Ju, M., Zhao, T., Wen, Q., Yu, W., Shah, N., Ye, Y., and Zhang, C · 2023
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Igb: Addressing the gaps in labeling, features, heterogeneity, and size of public graph datasets for deep learning research
Khatua, A., Mailthody, V. S., Taleka, B., Ma, T., Song, X., and Hwu, W.-m · 2023
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Link prediction with non-contrastive learning
Shiao, W., Guo, Z., Zhao, T., Papalexakis, E. E., Liu, Y., and Shah, N · 2023
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