Permutation-Invariant graph partitioning: How graph neural networks capture structural interactions?
- DOI
- 10.1016/j.neunet.2026.108869
- Published
- 2026-08
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.108869,
title = {Permutation-Invariant graph partitioning: How graph neural networks capture structural interactions?},
author = {Asela Hevapathige and Qing Wang},
year = {2026},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.108869},
url = {https://doi.org/10.1016/j.neunet.2026.108869}
}RIS
TY - JOUR TI - Permutation-Invariant graph partitioning: How graph neural networks capture structural interactions? AU - Asela Hevapathige AU - Qing Wang PY - 2026 JO - Neural Networks DO - 10.1016/j.neunet.2026.108869 UR - https://doi.org/10.1016/j.neunet.2026.108869 ER -
APA
Hevapathige, A., & Wang, Q. (2026). Permutation-Invariant graph partitioning: How graph neural networks capture structural interactions?. Neural Networks. https://doi.org/10.1016/j.neunet.2026.108869
Source records
- crossref · retrieved 2026-09-26T03:54:21.796Z