- Data-Driven Allocation of Vaccines for Controlling Epidemic Outbreaks
- On the Complexity of the Minimum Input Selection Problem for Structural Controllability
- Stability of Spreading Processes over Time-Varying Large-Scale Networks
- Moment-Based Spectral Analysis of Large-Scale Networks Using Local Structural Information
- Structural Analysis of Laplacian Spectral Properties of Large-Scale Networks
- Low-Order Spectral Analysis of the Kirchhoff Matrices for a Probabilistic Graph with a Prescribed Expected Degree Sequence
- Laplacian Spectral Properties of a Graph from Random Structural Sample
Understanding spreading processes in complex networks and designing control strategies to contain them are relevant problems in many different settings, such as epidemiology and public health. The aim of our research is to control spreading processes in networks by distributing protection resources throughout the nodes. In our study, we consider two types of containment resources: (i) Preventive resources able to protect (or ‘immunize’) nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node, such as antidotes in a viral infection. In our framework, we associate a cost to these resources and study the problem of finding the cost-optimal distribution of resources throughout the network to contain the spreading.
- Analysis and Control of Epidemics: A Survey of Spreading Processes in Complex Networks
- Optimal Resource Allocation for Control of Networked Epidemic Models
- Optimal Vaccine Allocation to Control Epidemic Outbreaks in Arbitrary Networks
- Traffic Optimization to Control Epidemic Outbreaks in Metapopulation Models
- A Convex Framework for Optimal Investment on Disease Awareness in Social Networks
- Detection and Isolation of Failures in Directed Networks of LTI Systems
- Detection and Isolation of Link Failures under Consensus Dynamics
- Failure Detection and Isolation in Integrator Networks
- Worst-Case Scenarios for Greedy, Centrality-Based Network Protection Strategies
- Bio-Inspired Framework for Allocation of Protection Resources in Cyber-Physical Networks
The reconstruction of networks of dynamical systems is an important task in many realms of science and engineering, including biology, physics and finance. The aim of our research is to propose several algorithms to reconstruct the structure of a directed dynamic networks. We have proposed an algorithm to find the Boolean structure of the unknown topology based on the analysis of power spectral properties of the network response when the inputs are wide-sense stationary (WSS) processes of unknown power spectral density (PSD). Apart from recovering the Boolean structure of the network, we have also proposed an algorithm to recover the exact structure of the network (including edge weights) when an eigenvalue-eigenvector pair of the connectivity matrix is known.
- Spectral Inference of Functional Connectivity in Brain Networks
- Topology Identification of Directed Dynamical Networks via Cross-Spectral Analysis
- Reconstruction of Directed Networks from Consensus Dynamics
- Cassiano Becker (Ph.D. candidate Sep 2013-)
- Ximing Chen (Ph.D. candidate Sep 2013-)
- Mahyar Fazlyab (Ph.D. candidate Sep 2013-)
- Zhen Xiang (Ph.D. candidate Sep 2014-)
- Christopher Cerezo Falco (Ph.D. candidate 2015-)
- Masaki Ogura (Nov 2014-)
- Ishaan Nerurkar
- Zhengwei Wu (M.Sc. 2012-2013). Thesis: "Spectral Analysis of Spreading Processes in Networks"
- David Sun (M.Sc. 2012-2015). Thesis: "Predicting Mean-Field Theory Accuracy on Real-World Networks"