Graph knowledge propagation is a way of spreading information through a network of connected items, called nodes, based on their relationships. Each node can share what it knows with its neighbours, helping the whole network learn more about itself. This method is used in computer science and artificial intelligence to help systems understand complex structures,…
Category: Graph-Based Learning
Graph Embedding Techniques
Graph embedding techniques are methods used to turn complex networks or graphs, such as social networks or molecular structures, into numerical data that computers can easily process. These techniques translate the relationships and connections within a graph into vectors or coordinates in a mathematical space. By doing this, they make it possible to apply standard…
Graph Signal Processing
Graph Signal Processing is a field that extends traditional signal processing techniques to data structured as graphs, where nodes represent entities and edges show relationships. Instead of working with signals on regular grids, like images or audio, it focuses on signals defined on irregular structures, such as social networks or sensor networks. This approach helps…
Graph-Based Inference
Graph-based inference is a method of drawing conclusions by analysing relationships between items represented as nodes and connections, or edges, on a graph. Each node might stand for an object, person, or concept, and the links between them show how they are related. By examining how nodes connect, algorithms can uncover hidden patterns, predict outcomes,…
Graph Knowledge Propagation
Graph knowledge propagation is a process where information or attributes are shared between connected nodes in a network, such as people in a social network or web pages on the internet. This sharing helps each node gain knowledge from its neighbours, allowing the system to learn or infer new relationships and properties. It is widely…
Graph-Based Predictive Analytics
Graph-based predictive analytics is a method that uses networks of connected data points, called graphs, to make predictions about future events or behaviours. Each data point, or node, can represent things like people, products, or places, and the connections between them, called edges, show relationships or interactions. By analysing the structure and patterns within these…
Privacy-Preserving Knowledge Graphs
Privacy-preserving knowledge graphs are data structures that organise and connect information while protecting sensitive or personal data. They use methods like anonymisation, access control, and encryption to ensure that private details are not exposed during data analysis or sharing. This approach helps organisations use the benefits of connected information without risking the privacy of individuals…
Graph-Based Feature Extraction
Graph-based feature extraction is a method used to identify and describe important characteristics or patterns from data that can be represented as a network or graph. In this approach, data points are seen as nodes and their relationships as edges, allowing complex connections to be analysed. Features such as node connectivity, centrality, or community structure…
Knowledge Mapping Techniques
Knowledge mapping techniques are methods used to visually organise, represent, and share information about what is known within a group, organisation, or subject area. These techniques help identify where expertise or important data is located, making it easier to find and use knowledge when needed. Common approaches include mind maps, concept maps, flowcharts, and diagrams…
Graph Knowledge Distillation
Graph Knowledge Distillation is a machine learning technique where a large, complex graph-based model teaches a smaller, simpler model to perform similar tasks. This process transfers important information from the big model to the smaller one, making it easier and faster to use in real situations. The smaller model learns to mimic the larger model’s…