Exploring biological network structures: aquatic food webs and contact networks

Complex networks are graphs that represent systems of interconnected components, distinguished by non-trivial structural features that set them apart from regular or random graphs. They model systems by assigning nodes to each entity of the system, connected by their interactions (edges), and usually exhibiting emergent patterns and properties that are not easily inferred from individual components. Two important types of networks in biology are food webs and contact networks, with the former modelling ``who eats whom'' in ecosystems and the latter modelling ``who meets whom'' in populations. Food webs are a specific type of complex networks that model energy and matter exchange in ecosystems. Understanding the patterns of these networks is important for identifying key species, assessing ecosystem health and stability, and predicting the effects of environmental disturbance. Contact networks represent one of the most important frameworks in fields such as epidemiology, where the particular structural properties of these networks have a significant impact on the dynamics of disease spread, making realistic models of these networks necessary to predict and control these dynamics. The objective of this thesis is twofold: first, to measure and analyze the patterns of aquatic food webs, aiming to gain deeper insights into their structural and functional properties; second, to propose a network model to incorporate group mixing for constructing more realistic contact networks.