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Topic:graph embedding

Graph embedding is a research area within machine learning and graph theory that focuses on representing graphs in a lower-dimensional space where their structural properties are preserved. The goal of graph embedding is to transform complex and high-dimensional graph data into low-dimensional vector representations that can be used for various machine learning tasks, such as node classification, link prediction, and graph clustering. By embedding graphs into a lower-dimensional space, it becomes easier to analyze and manipulate graph data, making it a powerful tool for analyzing and understanding complex networks. Some common techniques used in graph embedding include node embedding methods like node2vec and graph neural networks.