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 KM prediction model uses a graph neural network to create a 50-dimensional task-specific fingerprint of the substrate.
“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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Graph Transformer NetworksPreprint
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Learning Deep Generative Models of GraphsPreprint
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Deep SetsPreprint
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Graph networks as learnable physics engines for inference and control
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Graph-to-Sequence Learning using Gated Graph Neural Networks
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Graph Neural Networks Exponentially Lose Expressive Power for Node ClassificationPreprint
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Rethinking Knowledge Graph Propagation for Zero-Shot Learning
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Adaptive Sampling Towards Fast Graph Representation LearningPreprint
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Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchPreprint
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Signed Graph Convolutional Networks
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Graph Convolutional Networks with EigenPooling
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Self-citationAttributed Graph Clustering via Adaptive Graph Convolution
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Revisiting Graph Neural Networks: All We Have is Low-Pass FiltersPreprint
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Multi-label Zero-Shot Learning with Structured Knowledge Graphs
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Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach
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Exact Combinatorial Optimization with Graph Convolutional Neural NetworksPreprint
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Self-citationStochastic Training of Graph Convolutional Networks with Variance ReductionPreprint
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Self-citationGraphAF: a Flow-based Autoregressive Model for Molecular Graph GenerationPreprint
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Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
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Self-citationGraph Wavelet Neural NetworkPreprint
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NetGAN: Generating Graphs via Random WalksPreprint
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Graph-Bert: Only Attention is Needed for Learning Graph RepresentationsPreprint
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SIGN: Scalable Inception Graph Neural NetworksPreprint
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Deep Learning for Generic Object Detection: A Survey
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Simplifying Graph Convolutional NetworksPreprint
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Diffusion Models: A Comprehensive Survey of Methods and Applications
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Self-citationDeep Learning on Graphs: A Survey
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DeepGCNs: Can GCNs Go As Deep As CNNs?
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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
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“as Multi-Agent Deep Reinforcement Learning [288]–[290], Graph Neural Networks (GNNs) [291], [292], can also be exploited to facilitate this process.”
BackgroundGraph Neural Networks in Recommender Systems: A Survey
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- 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 · 2019: 108 · 2018: 2
- 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 › Computer Vision and Pattern Recognition › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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
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