In the last twenty years network science has proven its strength in modelling many real-world interacting systems as generic agents, the nodes, connected by pairwise edges. Yet, in many relevant cases, interactions are not pairwise but involve larger sets of nodes, at a time. These systems are thus better described in the framework of hypergraphs, whose hyperedges effectively account for multi-body interactions.
The modeling of the spreading of communicable diseases has experienced significant advances in the last decades. This has been possible due to the proliferation of data and the development of new methods to gather, mine and analyze it. A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of many possible heterogeneities and features that can be extracted from data.
We propose a new methodological framework for data-based network reconstruction, which accounts for the uncertainty about the connectivity [1]. The nodes of the networks considered in our study represent interacting dynamical systems which are the components of a complex system. The network reconstruction problem is to derive the topological structure of the interactions between the components, relying on temporal data produced by each component. A wealth of methods has been developed to reconstruct the network connectivity using observations about the component’s dynamic (see e.g. [2]).
The overall function of networked systems is known to be closely related to their structural properties. This structure, i.e., the nodes and the links, might undergo random failures or suffer targeted attacks. Depending on how and where these events occur, the global properties of the network can be severely affected or remain almost unaltered. This roughly defines the concept of network robustness or resilience. In this work we focus our analysis on cascade-based attacks, i.e., those attacks that, by removing a single node, cause disproportionate damage to a large part of the system.