1. Al-Rfou, R., Perozzi, B., Zelle, D.: Ddgk: learning graph representations for deep divergence graph kernels. In: The World Wide Web Conference, pp. 37–48 (2019)

2. Andersen, R., Borgs, C., Chayes, J., Hopcraft, J., Mirrokni, V.S., Teng, S.H.: Local computation of pagerank contributions. In: Algorithms and models for the web-graph, vol. 4863, pp. 150–165. Springer Berlin Heidelberg, Berlin, Heidelberg (2007)

3. Andersen, R., Chung, F., Lang, K.: Local graph partitioning using PageRank vectors. In: 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS’06), pp. 475–486. IEEE (2006)

4. Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: 29th Advances in Neural Information Processing Systems pp. 2001–2009 (2016)

5. Bojchevski, A., Klicpera, J., Perozzi, B., Kapoor, A., Blais, M., Rózemberczki, B., Lukasik, M., Günnemann, S.: Scaling graph neural networks with approximate pagerank. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2464–2473 (2020)

6. Candès, E.J., Li, X., Ma, Y., Wright, J.: Robust principal component analysis? J. ACM58(3), 1–37 (2011)

   Article MathSciNet  Google Scholar 

7. Chandrasekaran, V., Sanghavi, S., Parrilo, P.A., Willsky, A.S.: Rank-sparsity incoherence for matrix decomposition. SIAM J. Optim.21(2), 572–596 (2011)

   Article MathSciNet  Google Scholar 

8. Chen, Z., Bruna, J., Li, L.: Supervised community detection with line graph neural networks. In: 7th International Conference on Learning Representations (2019)

9. Chen, J., Ma, T., Xiao, C.: Fastgcn: fast learning with graph convolutional networks via importance sampling. In: International Conference on Learning Representations (2018)

10. Chen, M., Wei, Z., Ding, B., Li, Y., Yuan, Y., Du, X., Wen, J.R.: Scalable graph neural networks via bidirectional propagation. In: 33rd Advances in Neural Information Processing Systems (2020)

11. Chen, J., Zhu, J., Song, L.: Stochastic training of graph convolutional networks with variance reduction. Int. C. Mach. Learn.3, 1503–1532 (2018)

    Google Scholar 

12. Chiang, W.L., Liu, X., Si, S., Li, Y., Bengio, S., Hsieh, C.J.: Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 257–266 (2019)

13. Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized pagerank graph neural network. In: 9th International Conference on Learning Representations (2021)

14. Coudron, M., Lerman, G.: On the sample complexity of robust PCA. In: 25th Advances in Neural Information Processing Systems (2012)

15. Ding, M., Kong, K., Li, J., Zhu, C., Dickerson, J.P., Huang, F., Goldstein, T.: VQ-GNN: a universal framework to scale up graph neural networks using vector quantization. In: 34th Advances in Neural Information Processing Systems (2021)

16. Fey, M., Lenssen, J.E., Weichert, F., Leskovec, J.: GNNAutoScale: scalable and expressive graph neural networks via historical embeddings. In: 38th International Conference on Machine Learning. PMLR 139 (2021)

17. Fogaras, D., Rácz, B., Csalogány, K., Sarlós, T.: Towards scaling fully personalized pagerank: algorithms, lower bounds, and experiments. Inter. Math.2(3), 333–358 (2005)

    MathSciNet  Google Scholar 

18. Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)

19. Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., Leskovec, J., Barzilay, R., Battaglia, P., Bengio, Y., Bronstein, M., Günnemann, S., Hamilton, W., Jaakkola, T., Jegelka, S., Nickel, M., Re, C., Song, L., Tang, J., Welling, M., Zemel, R.: Open graph benchmark : datasets for machine learning on graphs. In: 33rd Advances in Neural Information Processing Systems (2020)

20. Huang, Z., Zhang, S., Xi, C., Liu, T., Zhou, M.: Scaling up graph neural networks via graph coarsening. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, vol. 1, pp. 675–684 (2021)

21. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)

22. Klicpera, J., Bojchevski, A., Günnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank. In: 7th International Conference on Learning Representations, pp. 1–15 (2019)

23. Li, X., Zhu, R., Cheng, Y., Shan, C., Luo, S., Li, D., Qian, W.: Finding global homophily in graph neural networks when meeting heterophily. In: 39th International Conference on Machine Learning (2022)

24. Liao, N., Luo, S., Li, X., Shi, J.: LD2: Scalable heterophilous graph neural network with decoupled embedding. In: Advances in Neural Information Processing Systems, vol. 36 (2023)

25. Liao, N., Mo, D., Luo, S., Li, X., Yin, P.: SCARA: scalable graph neural networks with feature-oriented optimization. Proc. VLDB Endowm.15(11), 3240–3248 (2022)

    Article  Google Scholar 

26. Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low-rank representation. In: 24th Advances in Neural Information Processing Systems (2011)

27. Lin, D., Wong, R.C.W., Xie, M., Wei, V.J.: Index-free approach with theoretical guarantee for efficient random walk with restart query. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 913–924 (2020)

28. Liu, H., Liao, N., Luo, S.: Simga: A simple and effective heterophilous graph neural network with efficient global aggregation. arXiv e-prints (2023)

29. Mo, D., Luo, S.: Agenda: robust personalized pageranks in evolving graphs. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1315–1324. ACM, Virtual Event Queensland Australia (2021)

30. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Tech. rep. (1999)

31. Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.J.P., Wang, K.: An overview of microsoft academic service (mas) and applications. In: Proceedings of the 24th International Conference on World Wide Web, pp. 243–246 (2015)

32. Sun, L., Dou, Y., Yang, C., Wang, J., Yu, P.S., He, L., Li, B.: Adversarial attack and defense on graph data: a survey. arXiv e-prints (2018)

33. Thekumparampil, K.K., Wang, C., Oh, S., Li, L.J.: Attention-based graph neural network for semi-supervised learning. arXiv e-prints (2018)

34. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: 8th International Conference on Learning Representations (2017)

35. Wang, H., He, M., Wei, Z., Wang, S., Yuan, Y., Du, X., Wen, J.R.: Approximate graph propagation. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 1, pp. 1686–1696. Association for Computing Machinery (2021)

36. Wang, K., Luo, S., Lin, D.: River of no return: graph percolation embeddings for efficient knowledge graph reasoning. arXiv e-prints (2023)

37. Wang, C., Pan, S., Long, G., Zhu, X., Jiang, J.: Mgae: marginalized graph autoencoder for graph clustering. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM ’17, p. 889–898. Association for Computing Machinery, New York, NY, USA (2017)

38. Wang, X., Zhang, M.: How powerful are spectral graph neural networks. In: 39th International Conference on Machine Learning (2022)

39. Wang, S., Yang, R., Xiao, X., Wei, Z., Yang, Y.: Fora: simple and effective approximate single-source personalized pagerank. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data MiningPart F1296, 505–514 (2017)

    Google Scholar 

40. Wang, S., Yang, R., Wang, R., Xiao, X., Wei, Z., Lin, W., Yang, Y., Tang, N.: Efficient algorithms for approximate single-source personalized PageRank queries. ACM Trans. Datab. Syst.44(4), 1–37 (2019)

    Article MathSciNet  Google Scholar 

41. Wu, H., Gan, J., Wei, Z., Zhang, R.: Unifying the global and local approaches: an efficient power iteration with forward push. In: Proceedings of the 2021 International Conference on Management of Data, vol. 1, pp. 1996–2008 (2021)

42. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: K. Chaudhuri, R. Salakhutdinov (eds.) Proceedings of the 36th International Conference on Machine Learning, vol. 97, pp. 6861–6871 (2019)

43. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neur. Netw. Learn. Syst.32(1), 4–24 (2021)

    Article MathSciNet  Google Scholar 

44. Yang, R., Shi, J., Xiao, X., Yang, Y., Liu, J., Bhowmick, S.S.: Scaling attributed network embedding to massive graphs. Proc. VLDB Endow.14(1), 37–49 (2021)

    Article  Google Scholar 

45. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)

46. Zeng, H., Zhou, H., Srivastava, A., Kannan, R., Prasanna, V.: Graphsaint: Graph sampling based learning method. In: International Conference on Learning Representations (2019)

47. Zhang, J., Zhang, H., Xia, C., Sun, L.: Graph-bert: only attention is needed for learning graph representations. arXiv e-prints (2020)

48. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. Adv. Neural Inf. Process. Syst.31, 5165–5175 (2018)

49. Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: a survey. IEEE Tran. Knowl. Data Eng.14(8), 1 (2020)

    Google Scholar 

50. Zhu, Z., Peng, J., Li, J., Chen, L., Yu, Q., Luo, S.: Spiking graph convolutional networks. In: 31th International Joint Conference on Artificial Intelligence (2022)

51. Zügner, D., Akbarnejad, A., Günnemann, S.: Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2847–2856 (2018)

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