Spanning Tree

Spanning trees, minimal connected subgraphs of a graph, are fundamental structures used to represent data relationships and solve optimization problems across diverse fields. Current research focuses on developing efficient algorithms for finding optimal spanning trees under various constraints, including privacy preservation, robustness to noise, and minimum size requirements, often employing techniques like minimum spanning tree (MST) compression, attention-based models (e.g., NeuroPrim), and novel heuristics for NP-hard problems. These advancements improve the efficiency and applicability of spanning trees in data analysis, distributed computing, and applications such as fake news mitigation and binary neural network compression, leading to more robust and scalable solutions.