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In several applications, the data can be naturally represented by graph structures. The simplest kind of graph structures are single nodes, and sequences, but in many application domains, the information is organized in more complex graph structures such as trees, acyclic graphs, or cyclic graphs.
In machine learning, the structured data is often associated with the goal of either supervised or unsupervised learning from examples such that a function maps a graph G and one of its nodes n to a vector of reals. In those applications, the goal consists of learning from examples a function that maps a graph G and one of its nodes n to a vector of reals:

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  credits .: Artificial Intelligence Research Group of Siena :.
 
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