Dynamic graph representation is a way of modelling and storing graphs where the structure or data can change over time. This approach allows for updates such as adding or removing nodes and edges without needing to rebuild the entire graph from scratch. It is often used in situations where relationships between items are not fixed…
Category: Graph-Based Learning
Graph-Based Anomaly Detection
Graph-based anomaly detection is a technique used to find unusual patterns or outliers in data that can be represented as networks or graphs, such as social networks or computer networks. It works by analysing the structure and connections between nodes to spot behaviours or patterns that do not fit the general trend. This method is…
Graph Signal Processing
Graph Signal Processing (GSP) is a field that studies how to analyse and process data that lives on graphs, such as social networks or transportation systems. It extends traditional signal processing, which deals with time or space signals, to more complex structures where data points are connected in irregular ways. GSP helps to uncover patterns,…
Heterogeneous Graph Attention
Heterogeneous graph attention is a method in machine learning that helps computers analyse and learn from complex networks containing different types of nodes and connections. Unlike standard graphs where all nodes and edges are the same, heterogeneous graphs have a mix, such as people, organisations, and products connected in various ways. The attention mechanism helps…
Graph Neural Network Scalability
Graph Neural Network scalability refers to the ability of graph-based machine learning models to efficiently process and learn from very large graphs, often containing millions or billions of nodes and edges. As graphs grow in size, memory and computation demands increase, making it challenging to train and apply these models without special techniques. Solutions for…
Knowledge Graph Reasoning
Knowledge graph reasoning is the process of drawing new conclusions or finding hidden connections within a knowledge graph. A knowledge graph is a network of facts, where each fact links different pieces of information. Reasoning uses rules or algorithms to connect the dots, helping computers answer complex questions or spot patterns that are not immediately…
Graph-Based Clustering
Graph-based clustering is a method of grouping items by representing them as points, called nodes, and connecting similar ones with lines, called edges, to form a network or graph. The method looks for clusters, which are groups of nodes that are more closely linked to each other than to the rest of the network. This…
Graph Autoencoders
Graph autoencoders are a type of machine learning model designed to work with data that can be represented as graphs, such as networks of people or connections between items. They learn to compress the information from a graph into a smaller, more manageable form, then reconstruct the original graph from this compressed version. This process…
Subgraph Matching Algorithms
Subgraph matching algorithms are methods used to find if a smaller graph, called a subgraph, exists within a larger graph. They compare the structure and connections of the nodes and edges to identify matches. These algorithms are important in fields where relationships and patterns need to be found within complex networks, such as social networks,…
Graph Pooling Techniques
Graph pooling techniques are methods used to reduce the size of graphs by grouping nodes or summarising information, making it easier for computers to analyse large and complex networks. These techniques help simplify the structure of a graph while keeping its essential features, which can improve the efficiency and performance of machine learning models. Pooling…