  Complex network theory is used to investigate the structure of meaningful
concepts in written texts of individual authors. Networks have been constructed
after a two phase filtering, where words with less meaning contents are
eliminated, and all remaining words are set to their canonical form, without
any number, gender or time flexion. Each sentence in the text is added to the
network as a clique. A large number of written texts have been scrutinized, and
its found that texts have small-world as well as scale-free structures. The
growth process of these networks has also been investigated, and a universal
evolution of network quantifiers have been found among the set of texts written
by distinct authors. Further analyzes, based on shufling procedures taken
either on the texts or on the constructed networks, provide hints on the role
played by the word frequency and sentence length distributions to the network
structure. Since the meaningful words are related to concepts in the author's
mind, results for text networks may uncover patterns in communication and
language processes that occur in the mind.
