Dynamic graph learning is a field of machine learning that focuses on analysing and understanding graphs whose structures or features change over time. Unlike static graphs, where relationships between nodes are fixed, dynamic graphs can have nodes and edges that appear, disappear, or evolve. This approach allows algorithms to model real-world situations where relationships and…

Graph Isomorphism Networks are a type of neural network designed to work with graph-structured data, such as social networks or molecules. They learn to represent nodes and their relationships by passing information along the connections in the graph. This approach helps the network recognise when two graphs have the same structure, even if the labels…

Message Passing Neural Networks (MPNNs) are a type of neural network designed to work with data structured as graphs, such as molecules or social networks. They operate by allowing nodes in a graph to exchange information with their neighbours through a series of message-passing steps. This approach helps the network learn patterns and relationships within…

Graph Convolutional Networks, or GCNs, are a type of neural network designed to work with data structured as graphs. Graphs are made up of nodes and edges, such as social networks where people are nodes and their connections are edges. GCNs help computers learn patterns and relationships in these networks, making sense of complex connections…

Graph Attention Networks, or GATs, are a type of neural network designed to work with data structured as graphs. Unlike traditional neural networks that process fixed-size data like images or text, GATs can handle nodes and their connections directly. They use an attention mechanism to decide which neighbouring nodes are most important when making predictions…