Neural Turing Machines are a type of artificial intelligence model that combines a neural network with an external memory bank. This setup allows the model to read from and write to its memory, similar to how a computer program works. It is designed to help machines learn tasks that require storing and recalling information over…
Temporal Graph Networks are a type of machine learning model that analyse data where relationships between items change over time. These models track not only the connections between objects, like people or devices, but also how these connections appear, disappear, or change as time passes. This helps to understand patterns and predict future events in…
Knowledge graph embeddings are a way to represent the information from a knowledge graph as numbers that computers can easily work with. In a knowledge graph, data is organised as entities and relationships, like a network of connected facts. Embeddings translate these complex connections into vectors, which are lists of numbers, so machine learning models…
Heterogeneous graph learning is a method in machine learning that works with graphs containing different types of nodes and connections. Unlike simple graphs where all nodes and edges are the same, heterogeneous graphs reflect real systems where entities and their relationships vary. This approach helps computers understand and analyse complex networks, such as social networks,…
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…
Spectral graph theory studies the properties of graphs using the mathematics of matrices and their eigenvalues. It looks at how the structure of a network is reflected in the numbers that come from its adjacency or Laplacian matrices. This approach helps to reveal patterns, connections, and clusters within networks that might not be obvious at…
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…
Neural combinatorial optimisation is a method that uses neural networks to solve complex problems where the goal is to find the best combination or arrangement from many possibilities. These problems are often difficult for traditional computers because there are too many options to check one by one. By learning from examples, neural networks can quickly…