The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Graph Representation Learning
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Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
(2014) · Cited by 33,419
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
Cited by
Multimodal data integration for oncology in the era of deep neural networks: a review
Waqas, A., Tripathi, A., Ramachandran, R. P., Stewart, P. A., & Rasool, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1408843 · Cited by 165
Graph representation learning for single-cell biology
Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55
Knowledge graphs: Introduction, history, and perspectives
Chaudhri, V. K., Baru, C., Chittar, N., Dong, X. L., Genesereth, M., Hendler, J., Kalyanpur, A., Lenat, D. B., Sequeda, J., Vrandečić, D., et al. (2022) · AI Magazine · doi:10.1002/aaai.12033 · Cited by 71
Weakly supervised joint whole-slide segmentation and classification in prostate cancer
Pati, P., Jaume, G., Ayadi, Z., Thandiackal, K., Bozorgtabar, B., Gabrani, M., & Goksel, O. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102915 · Cited by 37
Examining explainable clinical decision support systems with think aloud protocols
Anjara, S. G., Janik, A., Dunford-Stenger, A., Mc Kenzie, K., Collazo-Lorduy, A., Torrente, M., Costabello, L., & Provencio, M. (2023) · PLOS ONE · doi:10.1371/journal.pone.0291443 · Cited by 28
Metadata
- Authors
- Hamilton, W. L.
- Venue
- Synthesis Lectures on Artificial Intelligence and Machine Learning
- Publisher
- Springer International Publishing
- Published
- 2020
- ISSN
- 1939-4608 (ISSN-L)
- 1939-4616
- OpenAlex
- W3087257704
- Cited by
- 607
- References
- 56
- FWCI
- 27.84
- Type
- book
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Graph Representation Learning. https://app.cochat.ai/papers/10.1007/978-3-031-01588-5
- The paper
- Hamilton, W. L. (2020). Graph Representation Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. https://doi.org/10.1007/978-3-031-01588-5
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
