Graph Neural Networks16
AdvancedA class of models built for data with relationships - social networks, molecules, maps, and knowledge graphs.
Graph Attention Network
A GNN variant that learns to assign different importance weights to different neighbours during aggregation - letting the model focus on the most relevant connections rather than treating all neighbours equally.
Graph Contrastive Learning
A self-supervised approach to learning graph representations by training a model to recognise that augmented views of the same graph are similar, without requiring labelled data.
Graph Convolutional Network
The foundational GNN architecture that adapts convolutional operations to graph-structured data by treating each node's new representation as a weighted sum of its own features and its neighbours' features - making deep learning on graphs tractable.
All concepts
G
Graph Embedding
The process of mapping nodes, edges, or entire graphs into continuous vector spaces that capture their structural roles and relationships - enabling graph-structured data to be used by machine learning methods that require vector inputs.
Graph Generative Model
A deep generative model that learns to generate new graphs with realistic structural properties - used to design novel molecules, synthesise diverse network structures, and augment graph datasets for training.
Graph Neural Network
A class of deep learning models designed to operate directly on graph-structured data - learning representations that capture both the features of individual nodes and the structural relationships between them.
Graph Pooling
The operation of compressing a graph - many nodes and edges - into a fixed-size representation, enabling graph-level prediction tasks like molecular property prediction or graph classification.
Graph Transformer
A class of GNN architectures that applies Transformer-style self-attention to graph-structured data - allowing each node to attend to any other node in the graph, overcoming the locality limitations of standard message-passing GNNs.
K
Knowledge Graph
A structured representation of real-world entities and their relationships as a directed graph - enabling machines to reason over factual knowledge, answer questions, and make inferences by traversing a web of interconnected facts.
Knowledge Graph Completion
The task of predicting missing facts in a knowledge graph - inferring relationships between entities that are not yet recorded, based on existing patterns in the graph.
M
Message Passing
The computational paradigm underlying virtually all modern graph neural networks - iteratively passing information between neighbouring nodes and updating representations based on aggregated neighbourhood messages.
Molecular Graph Learning
The application of graph neural networks to molecular chemistry - representing molecules as atom-bond graphs and learning to predict properties like toxicity, solubility, and binding affinity directly from molecular structure.