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Everything about Graph neural network
Class of artificial neural networks
Graph neural networks (GNNs) are artificial neural networks designed for tasks whose inputs are graphs. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Graph neural networkA 5-minute orientation
- Skim a recent paper: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsInfluential work since 2022
- Pick up a classic book: The Graph Theoretical Approach in Brain Functional NetworksThe most-reprinted book on the subject
- Read the landmark paper: A Comprehensive Survey on Graph Neural NetworksCited 10,420 times
- Go sideways: Pooling layerA closely related topic
- Go sideways: Convolutional neural networkA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
A Comprehensive Survey on Graph Neural Networks
The Graph Neural Network Model
Graph neural networks: A review of methods and applications
Graph Neural Networks in Network Neuroscience
Graph Neural Network-Based Diagnosis Prediction
Curvature graph neural network
Reverse Graph Learning for Graph Neural Network
Decoupled semantic graph neural network for knowledge graph embedding
🆕 Influential since 2022
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Graph neural network for traffic forecasting: A survey
Graph Neural Networks in Recommender Systems: A Survey
Graph neural networks for materials science and chemistry
Molecular contrastive learning of representations via graph neural networks
📚 Books worth reading
The Graph Theoretical Approach in Brain Functional NetworksFabrizio De Vico Fallani, Fabrizio De Vico Fallani · 2010
Statistical And Evolutionary Analysis Of Biological NetworksMichael P. H. Stumpf · 2009
Advances in probabilistic graphical modelsLucas, Peter · 2007
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Pooling layer In neural networks, a pooling layer is a kind of network layer that downsamples and aggregates information that is dispersed among many vectors into fewer…
- Convolutional neural network A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep…
- Vision transformer A vision transformer (ViT) is a transformer designed for computer vision. A ViT decomposes an input image into a series of patches (rather than text into…
- Transformer (deep learning) In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data…
- Neural architecture search Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine…
- Steerable filter In image processing, a steerable filter is an orientation-selective filter that can be computationally rotated to any direction. Rather than designing…
- Representation learning In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations…
- Recursive neural network A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce…