Graphs can encode information from datasets that have a natural representation in terms of a network (for example datasets describing collaborations or social relations among individulas), as well as from data that can be mapped into graphs due to their intrinsic correlations, such as time series or images. Characterising the structure of complex networks at the micro and mesocale can thus be of fundamental importance to extract relevant information from our
data. We will present some algorithms useful to characterise the structure of particular classes of networks:
i) multiplex networks, describing systems where interactions of different
nature are involved,
and ii) visibility graphs, that can be extracted from time series.