Emergence and size of the giant component in clustered random graphs with a given degree distribution

Standard techniques for analyzing network models usually break down in the presence of clustering. Here we introduce a new analytic tool, the "free-excess degree" distribution, which extends the generating function framework, making it applicable for clustered networks (C>0). The methodology is general and provides a new expression for the threshold point at which the giant component emerges and shows that it scales as (1-C)-1. In addition, the size of the giant component may be predicted even for more complicated scenarios such as the removal of a fixed fraction of nodes at random

Webs of Trust: Choosing Who to Trust on the Internet

none1How to decide whether to engage in transactions with strangers? Whether we’re offering a ride, renting a room or apartment, buying or selling items, or even lending money, we need a degree of trust that the others will behave as they should. Systems like Airbnb, Uber, Blablacar, eBay and others handle this by creating systems where people initially start as untrusted, and they gain reputation over time by behaving well. Unfortunately, these systems are proprietary and siloed, meaning that all information about transactions becomes property of the company managing the systems, and that there are two types of barriers to entry: first, whenever new users enter a new system they will need to restart from scratch as untrusted, without the possibility of exploiting the reputation they gained elsewhere; second, new applications have a similar cold-start problem: young systems, where nobody has reputation yet, are difficult to kickstart. We propose a solution based on a web of trust: a decentralized repository of data about past interactions between users, without any trusted third party. We think this approach can solve the aforementioned issue, establishing a notion of trust that can be used across applications while protecting user privacy. Several problems require consideration, such as scalability and robustness, as well as the trade-off between privacy and accountability. In this paper, we provide an overview of issues and solutions available in the literature, and we discuss the directions to take to pursue this project.mixedDell'Amico M.Dell'Amico, M

Rethinking wastewater risks and monitoring in light of the COVID-19 pandemic

The COVID-19 pandemic has severely impacted public health and the worldwide economy. Converging evidence from the current pandemic, previous outbreaks and controlled experiments indicates that SARS-CoVs are present in wastewater for several days, leading to potential health risks via waterborne and aerosolized wastewater pathways. Conventional wastewater treatment provides only partial removal of SARS-CoVs, thus safe disposal or reuse will depend on the efficacy of final disinfection. This underscores the need for a risk assessment and management framework tailored to SARS-CoV-2 transmission via wastewater, including new tools for environmental surveillance, ensuring adequate disinfection as a component of overall COVID-19 pandemic containment