  Modularity plays an important role in brain networks' architecture and
influences its dynamics and the ability to integrate and segregate different
modules of cerebral regions. Alterations in community structure are associated
with several clinical disorders, specially schizophrenia, although its time
evolution is not clear yet. In the present work, we analyze fMRI functional
networks of $65$ healthy subjects (HC) and $44$ patients of schizophrenia (SZ),
$28$ of them in a chronic state (CR) of illness, and $16$ at early stage (ES).
We find clear differences in edges' weights distribution, networks density,
community structure consistency and robustness against edge removal. In
comparison to healthy subjects, we found that networks from SZ patients
exhibits wider weight distribution, larger overall connectivity, and are more
consistent in the community structure across subjects. We also showed that the
networks of SZ patients tend to be more robust to edge removal than healthy
subjects, while having lower network density. In the case of early stages
patients, we found that their networks exhibit topological features
consistently in between the ones obtained from the other two groups, resulting
in a tendency towards the chronic group state.
