Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular...

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...

social networks such as LinkedIn and Facebook. Recent developments in data science and machine learning, particularly in graph neural networks and representation...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

rock band Graph neural network, a class of neural network for processing data best represented by graph data structures Guerrilla News Network, a defunct...

neural networks, marking a departure from the typical focus on learning mappings between finite-dimensional Euclidean spaces or finite sets. Neural operators...

language processing. Recently[when?], it has also been introduced to graph neural networks applicable to non-grid data. Knowledge transfer from a large model...

article. Recently, Sepp Hochreiter argued that Graph Neural Networks "...are the predominant models of neural-symbolic computing" since "[t]hey describe the...

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...

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where...

machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as...

generative AI, and distributed deep learning. His book titled Graph Neural Networks: Foundations, Frontiers, and Applications has been published by...

Traditional deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), excel in processing data on regular grids...

The Open Neural Network Exchange (ONNX) [ˈɒnɪks] is an open-source artificial intelligence ecosystem of technology companies and research organizations...

neural networks by preventing complex co-adaptations on training data. They are an efficient way of performing model averaging with neural networks....

property graphs provide the basis for several machine-learning-based approaches to vulnerability discovery. In particular, graph neural networks (GNN) have...

Doina (6 December 2022). "Revisiting Heterophily For Graph Neural Networks". Advances in Neural Information Processing Systems. 35: 1362–1375. Luan, Sitao;...

In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up...

models, called message-passing neural networks (MPNNs), are graph neural networks. Treating molecules as three-dimensional graphs (where atoms are nodes and...

machine learning, the universal approximation theorems state that neural networks with a certain structure can, in principle, approximate any continuous...

ensemble modeling. Graph Neural Networks (GNNs): Supports models such as GraphSAGE, GCN, and GAT to learn from both node attributes and graph structure. Natural...

{\displaystyle a_{ij}} . A graph signal is simply a real-valued function on the set of nodes of the graph. In graph neural networks, graph signals are sometimes...

An artificial neural network (ANN) or neural network combines biological principles with advanced statistics to solve problems in domains such as pattern...

A spatial network (sometimes also geometric graph) is a graph in which the vertices or edges are spatial elements associated with geometric objects, i...

cross-modal graph neural networks (CMGNNs) that extend traditional graph neural networks (GNNs) to handle data from multiple modalities by constructing graphs that...

machine learning, a neural field (also known as implicit neural representation, neural implicit, or coordinate-based neural network), is a mathematical...

non-isomorphic graphs that WLpair cannot distinguish is given here. The theory behind the Weisfeiler Leman test may be applied in graph neural networks. In machine...

types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate...

Small-world network example Hubs are bigger than other nodes A small-world network is a graph characterized by a high clustering coefficient and low distances...