  Graphs have often been used to answer questions about the interaction between
real-world entities by taking advantage of their capacity to represent complex
topologies. Complex networks are known to be graphs that capture such
non-trivial topologies; they are able to represent human phenomena such as
epidemic processes, the dynamics of populations, and the urbanization of
cities. The investigation of complex networks has been extrapolated to many
fields of science, with particular emphasis on computing techniques, including
artificial intelligence. In such a case, the analysis of the interaction
between entities of interest is transposed to the internal learning of
algorithms, a paradigm whose investigation is able to expand the state of the
art in Computer Science. By exploring this paradigm, this thesis puts together
complex networks and machine learning techniques to improve the understanding
of the human phenomena observed in pandemics, pendular migration, and street
networks. Accordingly, we contribute with: (i) a new neural network
architecture capable of modeling dynamic processes observed in spatial and
temporal data with applications in epidemics propagation, weather forecasting,
and patient monitoring in intensive care units; (ii) a machine-learning
methodology for analyzing and predicting links in the scope of human mobility
between all the cities of Brazil; and, (iii) techniques for identifying
inconsistencies in the urban planning of cities while tracking the most
influential vertices, with applications over Brazilian and worldwide cities. We
obtained results sustained by sound evidence of advances to the state of the
art in artificial intelligence, rigorous formalisms, and ample experimentation.
Our findings rely upon real-world applications in a range of domains,
demonstrating the applicability of our methodologies.
