  Methods from statistical physics, such as those involving complex networks,
have been increasingly used in quantitative analysis of linguistic phenomena.
In this paper, we represented pieces of text with different levels of
simplification in co-occurrence networks and found that topological regularity
correlated negatively with textual complexity. Furthermore, in less complex
texts the distance between concepts, represented as nodes, tended to decrease.
The complex networks metrics were treated with multivariate pattern recognition
techniques, which allowed us to distinguish between original texts and their
simplified versions. For each original text, two simplified versions were
generated manually with increasing number of simplification operations. As
expected, distinction was easier for the strongly simplified versions, where
the most relevant metrics were node strength, shortest paths and diversity.
Also, the discrimination of complex texts was improved with higher hierarchical
network metrics, thus pointing to the usefulness of considering wider contexts
around the concepts. Though the accuracy rate in the distinction was not as
high as in methods using deep linguistic knowledge, the complex network
approach is still useful for a rapid screening of texts whenever assessing
complexity is essential to guarantee accessibility to readers with limited
reading ability
