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Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications.
S. Har-Peled and S. Mazumdar, “On coresets for k-means and k-median clustering,” in Proceedings of the thirty-sixth annual ACM symposium on Theory of computing , 2004, pp. 291–300
2004
Earlier work this paper cites.
M. Welling, “Herding dynamical weights to learn,” in Proceedings of the 26th Annual International Conference on Machine Learning , 2009, pp. 1121–1128
2009
Earlier work this paper cites.
R. Z. Farahani and M. Hekmatfar, Facility location: concepts, models, algorithms and case studies . Springer Science & Business Media, 2009
2009
Earlier work this paper cites.
B. Chandramouli, J. J. Levandoski, A. Eldawy, and M. F. Mokbel, “Streamrec: a real-time recommender system,” in Proceedings of the 2011 ACM SIGMOD International Conference on Management of data , 2011, pp. 1243–1246
2011
Earlier work this paper cites.
C. Chen, H. Yin, J. Yao, and B. Cui, “Terec: A temporal recommender system over tweet stream,” Proceedings of the VLDB Endowment , vol. 6, no. 12, pp. 1254–1257, 2013
2013
Earlier work this paper cites.
B. Zheng, K. Zheng, X. Xiao, H. Su, H. Yin, X. Zhou, and G. Li, “Keyword-aware continuous knn query on road networks,” in 2016 IEEE 32Nd international conference on data engineering (ICDE) . IEEE, 2016, pp. 871–882
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain , 2016
2016
Earlier work this paper cites.
Q. V. H. Nguyen, C. T. Duong, T. T. Nguyen, M. Weidlich, K. Aberer, H. Yin, and X. Zhou, “Argument discovery via crowdsourcing,” The VLDB Journal , vol. 26, pp. 511–535, 2017
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in International Conference on Machine Learning . PMLR, 2017, pp. 1263–1272
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , 2017
2017
Earlier work this paper cites.
S. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017 , 2017
2017
Earlier work this paper cites.
W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 1024–1034
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. Lucic, M. Faulkner, A. Krause, and D. Feldman, “Training gaussian mixture models at scale via coresets,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 5885–5909, 2017
2017
Earlier work this paper cites.
M. B. Cohen, C. Musco, and C. Musco, “Input sparsity time low-rank approximation via ridge leverage score sampling,” in Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms . SIAM, 2017, pp. 1758–1777
2017
Earlier work this paper cites.
A. Loukas and P. Vandergheynst, “Spectrally approximating large graphs with smaller graphs,” in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 , ser. Proceedings of Machine Learning Research, 2018
2018
Earlier work this paper cites.
W. Wang, H. Yin, Z. Huang, Q. Wang, X. Du, and Q. V. H. Nguyen, “Streaming ranking based recommender systems,” in The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval , 2018, pp. 525–534
2018
Earlier work this paper cites.
F. M. Castro, M. J. Marín-Jiménez, N. Guil, C. Schmid, and K. Alahari, “End-to-end incremental learning,” in Proceedings of the European conference on computer vision (ECCV) , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Wang, J.-Y. Zhu, A. Torralba, and A. A. Efros, “Dataset distillation,” ArXiv preprint , 2018
2018
Earlier work this paper cites.
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, “Session-based recommendation with graph neural networks,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, no. 01, 2019, pp. 346–353
2019
Earlier work this paper cites.
D. Wang, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, S. Yang, and Y. Qi, “A semi-supervised graph attentive network for financial fraud detection,” in 2019 IEEE International Conference on Data Mining (ICDM) . IEEE, 2019, pp. 598–607
2019
Cited alongside, same era.
A. Li, Z. Qin, R. Liu, Y. Yang, and D. Li, “Spam review detection with graph convolutional networks,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management , ser. CIKM ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 2703–2711
2019
Cited alongside, same era.
F. Wu, A. H. S. Jr., T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger, “Simplifying graph convolutional networks,” in Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA , 2019, pp. 6861–6871
2019
Cited alongside, same era.
G. Bravo Hermsdorff and L. Gunderson, “A unifying framework for spectrum-preserving graph sparsification and coarsening,” Advances in Neural Information Processing Systems , vol. 32, 2019
X. Xia, H. Yin, J. Yu, Q. Wang, G. Xu, and Q. V. H. Nguyen, “On-device next-item recommendation with self-supervised knowledge distillation,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 546–555
2022
Later among the works it cites.
S. Dai, Y. Yu, H. Fan, and J. Dong, “Spatio-temporal representation learning with social tie for personalized poi recommendation,” Data Science and Engineering , vol. 7, no. 1, pp. 44–56, 2022
2022
Later among the works it cites.
W. Zhang, Z. Yin, Z. Sheng, Y. Li, W. Ouyang, X. Li, Y. Tao, Z. Yang, and B. Cui, “Graph attention multi-layer perceptron,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022, pp. 4560–4570
2022
Later among the works it cites.
W. Jin, L. Zhao, S. Zhang, Y. Liu, J. Tang, and N. Shah, “Graph condensation for graph neural networks,” in International Conference on Learning Representations , 2022
2022
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2019
Cited alongside, same era.
W. Zhang, X. Miao, Y. Shao, J. Jiang, L. Chen, O. Ruas, and B. Cui, “Reliable data distillation on graph convolutional network,” in Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data , 2020, pp. 1399–1414
2020
Cited alongside, same era.
H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. K. Prasanna, “Graphsaint: Graph sampling based inductive learning method,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. A. Tailor, J. Fernandez-Marques, and N. D. Lane, “Degree-quant: Quantization-aware training for graph neural networks,” International Conference on Learning Representations (ICLR) , 2020
2020
Cited alongside, same era.
X. Sun, H. Yin, B. Liu, H. Chen, J. Cao, Y. Shao, and N. Q. Viet Hung, “Heterogeneous hypergraph embedding for graph classification,” in Proceedings of the 14th ACM international conference on web search and data mining , 2021, pp. 725–733
2021
Cited alongside, same era.
S. Zhang, H. Yin, T. Chen, Z. Huang, L. Cui, and X. Zhang, “Graph embedding for recommendation against attribute inference attacks,” in Proceedings of the Web Conference 2021 , 2021, pp. 3002–3014
2021
Cited alongside, same era.
Q. Wang, H. Yin, T. Chen, J. Yu, A. Zhou, and X. Zhang, “Fast-adapting and privacy-preserving federated recommender system,” The VLDB Journal , pp. 1–20, 2021
2021
Cited alongside, same era.
Y. Li, T. Chen, P.-F. Zhang, and H. Yin, “Lightweight self-attentive sequential recommendation,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management , 2021, pp. 967–977
2021
Cited alongside, same era.
Later among the works it cites.
W. Jin, X. Tang, H. Jiang, Z. Li, D. Zhang, J. Tang, and B. Yin, “Condensing graphs via one-step gradient matching,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022, pp. 720–730
2022
Later among the works it cites.
S. Si, F. Yu, A. S. Rawat, C.-J. Hsieh, and S. Kumar, “Serving graph compression for graph neural networks,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
G. Cazenavette, T. Wang, A. Torralba, A. A. Efros, and J.-Y. Zhu, “Dataset distillation by matching training trajectories,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 4750–4759
2022
Later among the works it cites.
Y. Tian, C. Zhang, Z. Guo, X. Zhang, and N. V. Chawla, “Nosmog: Learning noise-robust and structure-aware mlps on graphs,” NeurIPS 2022 Workshop: New Frontiers in Graph Learning , 2022
2022
Later among the works it cites.
R. Zheng, L. Qu, B. Cui, Y. Shi, and H. Yin, “Automl for deep recommender systems: A survey,” ACM Transactions on Information Systems , vol. 41, no. 4, pp. 1–38, 2023
2023
Closest in time.
J. Yu, H. Yin, X. Xia, T. Chen, J. Li, and Z. Huang, “Self-supervised learning for recommender systems: A survey,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
Y. Zang, R. Hu, Z. Wang, D. Xu, J. Wu, D. Li, J. Wu, and L. Ren, “Don’t ignore alienation and marginalization: Correlating fraud detection,” in 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 . International Joint Conferences on Artificial Intelligence, 2023, pp. 4959–4966
2023
Closest in time.
J. Long, T. Chen, Q. V. H. Nguyen, and H. Yin, “Decentralized collaborative learning framework for next poi recommendation,” ACM Transactions on Information Systems , vol. 41, no. 3, pp. 1–25, 2023
2023
Closest in time.
J. Long, T. Chen, Q. V. H. Nguyen, G. Xu, K. Zheng, and H. Yin, “Model-agnostic decentralized collaborative learning for on-device poi recommendation,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2023, pp. 423–432
2023
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X. Xia, J. Yu, Q. Wang, C. Yang, N. Q. V. Hung, and H. Yin, “Efficient on-device session-based recommendation,” ACM Transactions on Information Systems , vol. 41, no. 4, pp. 1–24, 2023
2023
Closest in time.
S. Xiao, D. Zhu, C. Tang, and Z. Huang, “Combining graph contrastive embedding and multi-head cross-attention transfer for cross-domain recommendation,” Data Science and Engineering , vol. 8, no. 3, pp. 247–262, 2023
2023
Closest in time.
X. Xia, J. Yu, G. Xu, and H. Yin, “Towards communication-efficient model updating for on-device session-based recommendation,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management , 2023, pp. 2795–2804
2023
Closest in time.
Y. Yang, H. Yin, J. Cao, T. Chen, Q. V. H. Nguyen, X. Zhou, and L. Chen, “Time-aware dynamic graph embedding for asynchronous structural evolution,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
B. Zhao and H. Bilen, “Dataset condensation with distribution matching,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 6514–6523
2023
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B. Hui, D. Yan, X. Ma, and W.-S. Ku, “Rethinking graph lottery tickets: Graph sparsity matters,” International Conference on Learning Representations (ICLR) , 2023
2023
Closest in time.