Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networks ...

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Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured ...

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI re- lies on maximizing mutual information between ...

LiDAR Situational Graphs (S-Graphs) is a ROS2 package for generating in real-time four-layered hierarchical factor graphs for single or multi-floor scenes. It reepresents a scene graph using 3D LiDAR ...

Graph Neural Networks (GNNs) are a recent family of Neural Network models specifically designed to harness the inherent structure and dependencies present in graph-structured data, revolutionizing the ...

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Learn how to compare and contrast BFS and DFS, two common graph algorithms for traversal and search. See examples, code, and tips for choosing the best one.

An index is a measure (often a numerical value) that can be representative of a particular pattern or state of a system. Climatologists monitor several indices, some ocean-based and some atmospheric, ...

In April 2025, researchers from the Universities of Liverpool, York, Sheffield and Nottingham released a report exploring contracts awarded by the UK government to companies to manage border security ...