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Graph Neural Networks (GNNs) excel in various graph learning tasks but face computational challenges when applied to large-scale graphs.
Algorithms for analysis and design of robust controllers
David, J · 1995
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Approximating st minimum cuts in õ (n 2) time
Benczúr, A. A. and Karger, D. R · 1996
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Bochner’s method for cell complexes and combinatorial ricci curvature
Forman · 2003
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Characterization of complex networks: A survey of measurements
Costa, L. d. F., Rodrigues, F. A., Travieso, G., and Villas Boas, P. R · 2007
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Scan: a structural clustering algorithm for networks
Xu, X., Yuruk, N., Feng, Z., and Schweiger, T. A · 2007
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Graph sparsification by effective resistances
Spielman, D. A. and Srivastava, N · 2008
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Local graph sparsification for scalable clustering
Satuluri, V., Parthasarathy, S., and Ruan, Y · 2011
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Spectral sparsification of graphs: theory and algorithms
Batson, J., Spielman, D. A., Srivastava, N., and Teng, S.-H · 2013
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Optimizing network robustness by edge rewiring: a general framework
Chan, H. and Akoglu, L · 2016
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Structure-preserving sparsification methods for social networks
Hamann, M., Lindner, G., Meyerhenke, H., Staudt, C. L., and Wagner, D · 2016
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Stochastic training of graph convolutional networks with variance reduction
Chen, J., Zhu, J., and Song, L · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Zhu, M. and Gupta, S · 2017
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Critical learning periods in deep networks
Achille, A., Rovere, M., and Soatto, S · 2018
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
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Provable and practical approximations for the degree distribution using sublinear graph samples
Eden, T., Jain, S., Pinar, A., Ron, D., and Seshadhri, C · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Lee, N., Ajanthan, T., and Torr, P. H · 2018
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Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., and Liotta, A · 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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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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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
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Sparse networks from scratch: Faster training without losing performance
Dettmers, T., Zettlemoyer, L., and Zhang · 2019
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Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 2019
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Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2019
Learning to drop: Robust graph neural network via topological denoising
Luo, D., Cheng, W., Yu, W., Zong, B., Ni, J., Chen, H., and Zhang, X · 2021
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Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?
Ma, X., Yuan, G., Shen, X., Chen, T., Chen, X., Chen, X., Liu, N., Qin, M., Liu, S., Wang, Z., et al · 2021
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Optimizing sparse matrix multiplications for graph neural networks
Qiu, S., You, L., and Wang, Z · 2021
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Multi-scale attributed node embedding
Rozemberczki, B., Allen, C., and Sarkar, R · 2021
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Graph sparsification via meta-learning
Wan, G. and Kokel, H · 2021
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Edge sparsification for graphs via meta-learning
Wan, G. and Schweitzer, H · 2021
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., et al · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Drawing early-bird tickets: Towards more efficient training of deep networks
You, H., Li, C., Xu, P., Fu, Y., Wang, Y., Chen, X., Baraniuk, R. G., Wang, Z., and Lin, Y · 2019
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Prone: Fast and scalable network representation learning
Zhang, J., Dong, Y., Wang, Y., Tang, J., and Ding, M · 2019
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Inductive matrix completion based on graph neural networks
Zhang, M. and Chen, Y · 2019
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Rigging the lottery: Making all tickets winners
Evci, U., Gale, T., Menick, J., Castro, P. S., and Elsen, E · 2020
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Pruning neural networks at initialization: Why are we missing the mark?
Frankle, J., Dziugaite, G. K., Roy, D. M., and Carbin, M · 2020
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Mest: Accurate and fast memory-economic sparse training framework on the edge
Yuan, G., Ma, X., Niu, W., Li, Z., Kong, Z., Liu, N., Gong, Y., Zhan, Z., He, C., Jin, Q., et al · 2021
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Graph structure learning with variational information bottleneck
Sun, Q., Li, J., Peng, H., Wu, J., Fu, X., Ji, C., and Philip, S. Y · 2022
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Searching lottery tickets in graph neural networks: A dual perspective
Wang, K., Liang, Y., Wang, P., Wang, X., Gu, P., Fang, J., and Wang, Y · 2022
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Early-bird gcns: Graph-network co-optimization towards more efficient gcn training and inference via drawing early-bird lottery tickets
You, H., Lu, Z., Zhou, Z., Fu, Y., and Lin, Y · 2022
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Demystifying graph sparsification algorithms in graph properties preservation
Chen, Y., Ye, H., Vedula, S., Bronstein, A., Dreslinski, R., Mudge, T., and Talati, N · 2023
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Mgnn: Graph neural networks inspired by distance geometry problem
Cui, G. and Wei, Z · 2023
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Revisiting pruning at initialization through the lens of ramanujan graph
Hoang, D., Liu, S., Marculescu, R., and Wang, Z · 2023
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Dynamic sparse training via balancing the exploration-exploitation trade-off
Huang, S., Lei, B., Xu, D., Peng, H., Sun, Y., Xie, M., and Ding, C · 2023
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Rethinking graph lottery tickets: Graph sparsity matters
Hui, B., Yan, D., Ma, X., and Ku, W.-S · 2023
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Fosr: First-order spectral rewiring for addressing oversquashing in gnns
Karhadkar, K., Banerjee, P. K., and Montufar, G · 2023
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Dspar: An embarrassingly simple strategy for efficient gnn training and inference via degree-based sparsification
Liu, Z., Zhou, K., Jiang, Z., Li, L., Chen, R., Choi, S.-H., and Hu, X · 2023
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The graph lottery ticket hypothesis: Finding sparse, informative graph structure
Tsitsulin, A. and Perozzi, B · 2023
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Dynamic sparse no training: Training-free fine-tuning for sparse llms
Zhang, Y., Zhao, L., Lin, M., Sun, Y., Yao, Y., Han, X., Tanner, J., Liu, S., and Ji, R · 2023
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Graph lottery ticket automated
Zhang, G., Wang, K., Huang, W., Yue, Y., Wang, Y., Zimmermann, R., Zhou, A., Cheng, D., Zeng*, J., and Liang*, Y · 2024
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