The DOI matches its OpenAlex record.
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
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Citations
Cited by
Heterogeneous Graph Attention Network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
Graph embedding techniques, applications, and performance: A survey
Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801
Modeling polypharmacy side effects with graph convolutional networks
Zitnik, M., Agrawal, M., & Leskovec, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty294 · Cited by 1,501
Graph neural network for traffic forecasting: A survey
Jiang, W., & Luo, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117921 · Cited by 1,316
Metadata
- Authors
- Silva, G., Rodrigues, M., Teixeira, A., & Amorim, M.
- Venue
- DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
- Publisher
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Published
- 2024
- OpenAlex
- W4294558607
- Cited by
- 5,183
- References
- 0
- FWCI
- 320.72
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks. https://app.cochat.ai/papers/10.4230/lipics.sea.2024.20
- The paper
- Silva, G., Rodrigues, M., Teixeira, A., & Amorim, M. (2024). Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). https://doi.org/10.4230/lipics.sea.2024.20
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.
