Feature Processing on Artificial Graph Node Features for Classification with Graph Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56310-2_17
Reference18 articles.
1. Zhou, J., Cui, G., Hu, S., et al.: Graph neural networks: a review of methods and applications. AI Open 1, 57–81 (2020). https://doi.org/10.1016/j.aiopen.2021.01.001
2. Duong, C.T., Hoang, T.D., Dang, H.T.H., et al.: On Node Features for Graph Neural Networks (2019)
3. Thang, D.C., Dat, H.T., Tam, N.T., et al.: Nature vs. nurture: feature vs. structure for graph neural networks. Pattern Recogn. Lett. 159, 46–53 (2022). https://doi.org/10.1016/j.patrec.2022.04.036
4. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
5. Cui, H., Lu, Z., Li, P., Yang, C.: On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs (2022)
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