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Graph neural networks: A review of methods and applications
Open accessAbstract
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.
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“The K M prediction model uses a graph neural network (GNN) 71 , 72 to create a 50-dimensional task-specific fingerprint of the substrate.”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [71]
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Metadata
- Authors
- Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M.
- Affiliations
- Tsinghua University (CN)
- Beijing University of Posts and Telecommunications (CN)
- Tencent (China) (CN)
- Corresponding author
- Liu, Zhiyuan
- ORCID
- Venue
- AI Open
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 1
- Pages
- 57-81
- ISSN
- 2666-6510 (ISSN-L)
- OpenAlex
- W3152893301
- Cited by
- 6,880
- References
- 311
- Citations by year
- 2026: 960 · 2025: 1372 · 2024: 1378 · 2023: 1123 · 2022: 872 · 2021: 702 · 2020: 363
- FWCI
- 300.75
- Type
- journal article
- Language
- en
- Update notices
- Open access
- gold
- doi.org
- Licence
- Locations
- AI Open (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- graph neural networks, graph convolutional networks, graph attention networks, message passing, graph reasoning, molecular fingerprints, protein interface prediction, disease classification, dependency trees, scene graph
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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