Graph embedding is a technique used to represent the nodes, edges, or entire graphs in a continuous vector space. The main idea behind graph embedding is to map discrete graph structures into a lower-dimensional space such that the semantic information and relationships within the graph are preserved as much as possible. This representation can then be used for various machine learning tasks, such as classification, clustering, or visualization. ### Key Concepts: 1. **Nodes and Edges**: In a graph, nodes represent entities (e.
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