Technology and the built world

Granular (software)

Granular is a farm management software company founded in San Francisco in 2014, acquired by DuPont for a reported $300 million in 2017 and now owned by Corteva Agriscience.

Granular activated carbon

Granular activated carbon (GAC) is a treatment material of porous carbon particles that removes dissolved contaminants from water and air by adsorption, widely used for organic contaminants and PFAS.

Granular activated carbon adsorption

Granular activated carbon adsorption is a water treatment process in which dissolved contaminants attach to porous carbon granules with surface areas of about 1,000 square meters per gram.

Granular computing is an umbrella term for theories and techniques that solve complex problems using information granules, collections of similar objects, an approach introduced by Lotfi Zadeh in 1979.

Oct 22, 2026

Granular metamaterials

Granular metamaterials are engineered materials whose unusual wave properties arise from ordered arrangements of discrete grains in contact, supporting applications like vibration absorption and shock protection.

Granville Gee Bee Model R Super Sportster

Granville Gee Bee Model R Super Sportster (R-1, R-2): 1932 Granville Brothers racing aircraft from Springfield, MA; Doolittle won the 1932 Thompson Trophy at 252.7 mph and set a 296.287 mph landplane record; both airframes crashed in 1933.

Granville's vibrator

Granville's vibrator is an electromechanical percuteur invented in the early 1880s by English physician Joseph Mortimer Granville to treat neuralgia, manufactured in London by J. Weiss & Son.

Grapeshot

Grapeshot was an antipersonnel artillery projectile of clustered iron balls fired from cannon, scattering like a shotgun blast; it was replaced by canister in field service during the 1800s.

Graph (abstract data type)

A graph, also known as a graph data structure, is an abstract data type that stores vertices and edges, supporting traversal algorithms like breadth-first and depth-first search.

Graph attention network

A graph attention network (GAT) is a neural network architecture for machine learning on graphs that weights neighbor features with learned attention, introduced at ICLR 2018.

Graph autoencoder

A graph autoencoder (GAE) is a neural network that encodes graphs into node embeddings and reconstructs the adjacency matrix, supporting unsupervised tasks like link prediction and node clustering.

Graph coloring

Graph coloring assigns colors to a graph's vertices so adjacent vertices get different colors, a central problem from map coloring; the four color theorem and scheduling are key examples.

Graph contrastive learning

Graph contrastive learning is a self-supervised method that learns node and graph representations without labels by contrasting augmented views, feeding tasks like node classification, graph classification, and drug discovery.

Graph convolutional network

A graph convolutional network (GCN) is a neural network architecture that applies convolution over graph-structured data, introduced by Kipf and Welling in 2016 and used chiefly for node classification and recommendation.

Graph database

A graph database is a database management system that stores data as nodes connected by edges and queries it by traversing connections rather than joining tables.

Graph filter

A graph filter is a linear operator that processes signals on graph vertices, typically a polynomial of the graph shift operator, and is the basic building block of graph signal processing.

Graph isomorphism network

A graph isomorphism network (GIN) is a graph neural network architecture that updates each node by summing neighbor features through an MLP, matching the 1-WL isomorphism test's power.

Graph kernel

A graph kernel is a positive semidefinite similarity function between graphs, letting kernel methods such as support vector machines classify graph-structured data like molecules and proteins.

Graph neural network

A graph neural network (GNN) is a neural network model that operates directly on graph-structured data, proposed in 2005 and extended to molecules, social networks, and traffic forecasting.

Graph of Thoughts

Graph of Thoughts (GoT) is a prompting framework for large language models, introduced by Besta et al. in 2023, that represents reasoning as an arbitrary graph of thoughts.

Graph rewriting

Graph rewriting is a family of formal methods in computer science that transform graphs by finding a pattern subgraph in a host graph and replacing it with a replacement.

Graph sampling

Graph sampling is a family of algorithms that randomly select nodes, edges, or subgraphs from a large graph to estimate statistics like degree and clustering when full processing is infeasible.

Graph search

Graph search is a family of algorithms that systematically explore the vertices and edges of a graph to find a target node, a path, or a structure.

Graph structure learning

Graph structure learning (GSL) is a machine learning approach that jointly learns a graph's connectivity, as an adjacency matrix, with a downstream task like node classification.

Graph transformer network

A graph transformer is a neural network that applies transformer-style self-attention to graph-structured data, adding positional and structural encodings or message-passing layers; Graphormer won the 2021 OGB Large-Scale Challenge for molecular property prediction.