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In many application domains, the amount of available data increased so much that humans need help from automatic computerized methods for extracting relevant information. Moreover, it is becoming more and more common to store data that possess inherently structural or relational characteristics. These types of data are best represented by graphs, which can very naturally represent entities, their attributes, and their relationships to other entities. In this article, we review the state of the art in graph mining, and we present advances in processing trees and graphs by two computational intelligence classes of methods, namely neural networks and kernel methods.