A graph kernel is a method used in machine learning and pattern recognition that measures the similarity between two graphs. Graphs are data structures composed of nodes (or vertices) and edges connecting these nodes. They can represent various types of data, such as social networks, molecular structures, and more. Graph kernels are particularly useful for tasks involving graph-structured data, where traditional vector-based methods are not applicable.
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