Category: Graph-Based Learning

Temporal Graph Embedding

Temporal graph embedding is a method for converting nodes and connections in a dynamic network into numerical vectors that capture how the network changes over time. These embeddings help computers understand and analyse evolving relationships, such as friendships or transactions, as they appear and disappear. By using temporal graph embedding, it becomes easier to predict…

Graph-Based Recommendation Systems

Graph-Based Recommendation Systems use graphs to model relationships between users, items, and other entities. In these systems, users and items are represented as nodes, and their interactions, such as likes or purchases, are shown as edges connecting them. By analysing the structure of these graphs, the system can find patterns and suggest items to users…

Graph Embedding Propagation

Graph embedding propagation is a technique used to represent nodes, edges, or entire graphs as numerical vectors while sharing information between connected nodes. This process allows the relationships and structural information of a graph to be captured in a format suitable for machine learning tasks. By propagating information through the graph, each node’s representation is…

Federated Knowledge Graphs

Federated knowledge graphs are systems that connect multiple independent knowledge graphs, allowing them to work together without merging all their data into one place. Each knowledge graph in the federation keeps its own data and control, but they can share information through agreed connections and standards. This approach helps organisations combine insights from different sources…

Probabilistic Graphical Models

Probabilistic Graphical Models are mathematical structures that use graphs to represent relationships between random variables. Each node in the graph stands for a variable, and the connections show how these variables influence each other. They help to break down complex systems into manageable parts, making it easier to understand and compute probabilities for different scenarios.

Temporal Graph Networks

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

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

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,…