The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Scaling Graph Neural Networks with Approximate PageRank
Open accessAbstract
Graph neural networks (GNNs) have emerged as a powerful approach for solving many network mining tasks. However, learning on large graphs remains a challenge -- many recently proposed scalable GNN approaches rely on an expensive message-passing procedure to propagate information through the graph. We present the PPRGo model which utilizes an efficient approximation of information diffusion in GNNs resulting in significant speed gains while maintaining state-of-the-art prediction performance. In addition to being faster, PPRGo is inherently scalable, and can be trivially parallelized for large datasets like those found in industry settings.
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Cites
Deep Residual Learning for Image Recognition
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Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
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The graph neural network model
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Cited by
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
Multi-Scale attributed node embedding
Rozemberczki, B., Allen, C., & Sarkar, R. (2021) · Journal of Complex Networks · doi:10.1093/comnet/cnab014 · Cited by 640
A Survey on Text Classification Algorithms: From Text to Predictions
Gasparetto, A., Marcuzzo, M., Zangari, A., & Albarelli, A. (2022) · Information · doi:10.3390/info13020083 · Cited by 253
Metadata
- Authors
- Bojchevski, A., Gasteiger, J., Perozzi, B., Kapoor, A., Blais, M., Rózemberczki, B., Lukasik, M., & Günnemann, S.
- Venue
- Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2020-08-20
- Pages
- 2464-2473
- OpenAlex
- W3039500550
- Cited by
- 371
- References
- 68
- FWCI
- 21.97
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Bojchevski, A., Gasteiger, J., Perozzi, B., Kapoor, A., Blais, M., Rózemberczki, B., Lukasik, M., & Günnemann, S. (2020). Scaling Graph Neural Networks with Approximate PageRank. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3394486.3403296
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
