Graph-based modelling is a way of representing data, objects, or systems using graphs. In this approach, items are shown as points, called nodes, and the connections or relationships between them are shown as lines, called edges. This method helps to visualise and analyse complex networks and relationships in a clear and structured way. Graph-based modelling…
Graph predictive systems are tools or models that use the structure of graphs, which are networks of connected points, to make predictions or forecasts. These systems analyse the relationships and connections between items, such as people, places, or things, to predict future events or behaviours. They are often used when data is naturally structured as…
Graph signal modelling is the process of representing and analysing data that is spread out over a network or graph, such as social networks, transport systems or sensor grids. Each node in the graph has a value or signal, and the edges show how the nodes are related. By modelling these signals, we can better…
Graph predictive modelling is a type of data analysis that uses the connections or relationships between items to make predictions about future events or unknown information. It works by representing data as a network or graph, where items are shown as points and their relationships as lines connecting them. This approach is especially useful when…
Graph knowledge modelling is a way to organise and represent information using nodes and relationships, much like a map of connected points. Each node stands for an item or concept, and the links show how these items are related. This approach helps computers and people understand complex connections within data, making it easier to search,…
Graph predictive analytics is a method that uses networks of connected data, called graphs, to forecast future outcomes or trends. It examines how entities are linked and uses those relationships to make predictions, such as identifying potential risks or recommending products. This approach is often used when relationships between items, people, or events provide valuable…
Graph signal analysis is a method for studying data that is spread over the nodes of a graph, such as sensors in a network or users in a social network. It combines ideas from signal processing and graph theory to understand how data values change and interact across connected points. This approach helps identify patterns,…
Graph-based analytics is a way of analysing data by representing it as a network of connected points, called nodes, and relationships, called edges. This approach helps to reveal patterns and connections that might be hard to spot with traditional tables or lists. It is especially useful for understanding complex relationships, such as social networks, supply…
Graph feature extraction is the process of identifying and collecting important information from graphs, which are structures made up of nodes and connections. This information can include attributes like the number of connections a node has, the shortest path between nodes, or the overall shape of the graph. These features help computers understand and analyse…
Graph-based prediction is a method of using data that is organised as networks or graphs to forecast outcomes or relationships. In these graphs, items like people, places, or things are represented as nodes, and the connections between them are called edges. This approach helps uncover patterns or make predictions by analysing how nodes are linked…