ArticleComputational social networks2019
Graph convolutional networks: a comprehensive review.
Article in Computational social networks, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 212 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
212 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- RedoxDB and RedEx: closing the DFT-experiment domain gap in redox potential prediction with transfer learning.Chemical science · 2026Article
- Topology-Guided Multi-Level Graph Learning: A Unified Framework for Molecular Property Prediction.Molecules (Basel, Switzerland) · 2026Article
- PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models.Briefings in bioinformatics · 2026Article
- M2-PRNet: multi-scale and multi-modal learning for protein-RNA binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network.Journal of imaging informatics in medicine · 2026Article
- High-Quality Predicted Metabolite-Pathway Annotations Expand Pathway Coverage and Increase Enriched Pathway Detection Across Metabolomics Datasets.Metabolites · 2026Article
- Smiles-based bioactivity prediction through molecular encoder selection and data augmentation.Journal of cheminformatics · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- Adaptive Self-Attention Graph Pooling for Drug-Target Affinity Prediction.International journal of molecular sciences · 2026Article
- Measuring the spatial lag effect of Tobler's First Law of Geography in Earth system predictive learning.Scientific reports · 2026Article
- Real-world and computational identification of herbal candidates associated with adverse event patterns in glucagon-like peptide-1 therapy for obesity.Scientific reports · 2026Article
- Transforming Molecular Science With Large Language Models: From Molecule Understanding to Autonomous Scientific Discovery.Chemistry, an Asian journal · 2026Review
- Identification of Major Depressive Disorder Using Multiple Functional Connection Patterns.CNS neuroscience & therapeutics · 2026Article
- Machine Learning-Aided Drug Repurposing for Screening COX-2 Inhibitors from Traditional Chinese Medicines.Pharmaceuticals (Basel, Switzerland) · 2026Article
- PRIME: An evaluation framework for protein representation inference and generalization in viral mutation space.BMC genomics · 2026Article
- Moving from table to graph in physics-informed spatio-temporal symbolic regression.Scientific reports · 2026Article
- Article
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- Carcinogenicity prediction via multi-task learning of cross-organ representations with attention mechanisms.Briefings in bioinformatics · 2026Article
152 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, and thus allows to harvest more insights compared to analyzing data in isolation. However, it is often very challenging to solve the learning problems on graphs, because (1) many types of data are not originally structured as graphs, such as images and text data, and (2) for graph-structured data, the underlying connectivity patterns are often complex and diverse. On the other hand, the representation learning has achieved great successes in many areas. Thereby, a potential solution is to learn the representation of graphs in a low-dimensional Euclidean space, such that the graph properties can be preserved. Although tremendous efforts have been made to address the graph representation learning problem, many of them still suffer from their shallow learning mechanisms. Deep learning models on graphs (e.g., graph neural networks) have recently emerged in machine learning and other related areas, and demonstrated the superior performance in various problems. In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models. First, we group the existing graph convolutional network models into two categories based on the types of convolutions and highlight some graph convolutional network models in details. Then, we categorize different graph convolutional networks according to the areas of their applications. Finally, we present several open challenges in this area and discuss potential directions for future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.