Moving beyond graph homophily: Disentangled node representation for graph neural networks.
- DOI
- 10.1016/j.neunet.2026.109799
- Published
- 2026 Oct 26
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109799,
title = {Moving beyond graph homophily: Disentangled node representation for graph neural networks.},
author = {Fang S and Yang Z and Cai Y and Huang Y and Min X},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109799},
url = {https://doi.org/10.1016/j.neunet.2026.109799}
}RIS
TY - JOUR TI - Moving beyond graph homophily: Disentangled node representation for graph neural networks. AU - Fang S AU - Yang Z AU - Cai Y AU - Huang Y AU - Min X PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109799 UR - https://doi.org/10.1016/j.neunet.2026.109799 ER -
APA
S, F., Z, Y., Y, C., Y, H., & X, M. (2026). Moving beyond graph homophily: Disentangled node representation for graph neural networks.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109799
Source records
- pubmed · retrieved 2026-11-10T13:29:07.444Z