This work deals with the use of the Network Robustness Simulator (NRS) to assess and compare the robustness of a set of networks. The NRS is able to compute a number of graph metrics which are put together in a single value $R^*$ by using the principal component analysis and generating heat maps as a visual result. The main features of the attack models are explained as main tool to analyze the robustness in a dynamic way.

Currently NRS implements random, targeted, epidemic and cascade failures. The main characteristics of these models are addressed.

Selecting sampling points to monitor traces of SARS-CoV-2 in sewage at the intra-urban scale is no trivial task given the complexity of the networks and the multiple technical, economic and socio-environmental constraints involved. This paper proposes two algorithms for the automatic selection of sampling locations in sewage networks. The algorithms are based on graph-theory and are coupled to a greedy optimization algorithm. The usefulness of the algorithms is illustrated in the case study of Girona (NE Iberian Peninsula, 100,000 inhabitants). The results show that the algorithms are able to automatically propose locations for a given number of stations. For the dynamic sensor placement, we demonstrate that assigning infection probabilities to each manhole as a function of the number of inhabitants connected reduces the number of iterations required to detect the zero patient and the hot spot areas.

During the talk a brief technical explanation about how the Network Robustness Simulator (NRS) has been implemented is introduced. Then a demonstration is done, showing how the NRS automatically executes a set of simulations and how the visualizer is able to deal with both single network and in interdependent networks backgrounds.

TBA

Current trends in extreme weather events or man-made stressors show that traditional risk management approaches fall short in effectively coping with disruptions. New management and design approaches that accept those disruptions are unavoidable, yet manageable events are needed to deal with these challenges. Consequently, the resilience of critical infrastructures has become a topic of interest in future strategic plans and design requirements. As a vital component of critical infrastructures, water networks' resilience and recovery processes need further investigation to ensure communities' access to a safe and secure water supply. In this presentation, I will discuss various disruption scenarios that impact operations of water networks with varying topologies. Furthermore, an overview of a simulation-based methodology will be provided to assess the system response and resilience. A set of strategies to increase water networks' operational performance and reduce the impacts of disruptions will be presented. Finally, I will discuss knowledge gaps and future research outlook for assessing the resilience of water networks.

Transport and logistics systems are the blood vessels of our economy, catering for primary mobility needs of people and firms. In this talk I explore robustness issues in these networks, focusing on system level impact analysis. I introduce the main impact mechanisms of disturbances, including common models used to quantify these impacts. I describe some recent cases of empirical, quantitative analysis of robustness of complex networks. Finally I propose a list of research issues that require co-operation between scientists specialised in complexity analysis and in transportation system analysis.

Large scale emergencies such as a large scale flooding or a devastating storm can severely impact Critical Infrastructures (CI). Since a subsequent disruption or even destruction of CI may increase the severity of an emergency, it is important for emergency management organizations to include an impact assessment of CI in their risk management, emergency planning, and emergency preparation.

In this presentation, I will discuss a methodology for emergency management organizations to assess the impact of CI disruptions. The methodology takes into account common cause failures affecting multiple CI concurrently, cascading effects as well as the potential disruptive impact on emergency management operations. The methodology builds on earlier CI assessment approaches at the national level and has been tested in a case study.

The main goal of my presentation is to briefly describes ICRA activities and then to focus on the description of a tool to support planning of water reuse networks in cities, including its treatment and the (re)distribution of the reclaimed water. The routing of the networks is designed using graph theory algorithms, combining terrain characteristics, including parcel and building usages, elevation and slope. Besides, the presentation also illustrates the usefulness of the tool when testing it in real cities. Construction costs and benefits in terms of water savings are estimated for each scenario. Finally, the optimal water network can be provided when a limited budget is available.

In this talk we will consider an SEIR epidemic model with age groups and mass vaccination where immunity is short lived and, hence, continuous vaccination campaigns are needed. The model is fed with demographic characteristics of several countries as well as with their specific age-group contact patterns in order to evaluate the critical vaccination rates for each each group. We will show how such demographic heterogeneity has a non-negligible impact on the optimal (but inequitable!) distribution of vaccine.

Network recoverability refers to the ability of a network to return to a desired performance level after suffering malicious attacks or random failures. A system is controllable if it can be driven from any arbitrary state to any desired state in finite time under the control of the driver nodes which are attached to external inputs. We use the minimum number of driver nodes as the R-value, which is a typical metric to denote the network controllability. We investigate the recoverability of network controllability in two recovery scenarios: (1) only the links which are damaged in the failure process can be recovered and (2) links can be established between any pair of nodes that have no link between them after the failure process. We propose analytical approximations for two recovery scenarios by using generating functions. Results show that our approximations fit well with simulation results both in synthetic networks and some real-world networks, such as swarm signaling networks and communication networks.

Determining a set of "important" nodes in a network constitutes a basic endeavour in network science. Inspired by electrical flows in a resistor network, we study the diagonal element $Q_{jj}^{\dagger}$ of the pseudo-inverse matrix $Q^{\dagger}$ of the weighted Laplacian matrix of the graph $G$. We propose a new graph metric that complements the effective graph resistance $R_{G}$ and that specifies the heterogeneity of the nodal spreading capacity in a graph. We also highlight an exact relation between "structure" and "function" of a network, based on the effective resistance matrix of a graph.