Epidemic Spreading in Weighted Networks: An Edge-Based Mean-Field Solution

Weight distribution largely impacts the epidemic spreading taking place on top of networks. This paper studies a susceptible-infected-susceptible model on regular random networks with different kinds of weight distributions. Simulation results show that the more homogeneous weight distribution leads to higher epidemic prevalence, which, unfortunately, could not be captured by the traditional mean-field approximation. This paper gives an edge-based mean-field solution for general weight distribution, which can quantitatively reproduce the simulation results. This method could find its applications in characterizing the non-equilibrium steady states of dynamical processes on weighted networks.Comment: 7 pages, 5 figure

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

Epidemics in Networks: Modeling, Optimization and Security Games

Epidemic theory has wide range of applications in computer networks, from spreading of malware to the information dissemination algorithms. Our society depends more strongly than ever on such computer networks. Many of these networks rely to a large extent on decentralization and self-organization. While decentralization removes obvious vulnerabilities related to single points of failure, it leads to a higher complexity of the system. A more complex type of vulnerability appears in such systems. For instance, computer viruses are imminent threats to all computer networks. We intend to study the interaction between malware spreading and strategies that are designed to cope with them. The main goals of this thesis are: 1. to analyze influence of network topology on infection spread 2. to determine how topology can be used for network protection 3. to formulate and study optimization of malware protection problem with respect to topology 4. to investigate non-cooperative game of security We used analytical tools from various fields to answer these questions. First of all, we have developed homogeneous and heterogeneous N-intertwined, susceptible - infected - susceptible (SIS) model for virus spread. This model is used to determine the influence of topology on the spreading process. For the N-intertwined model, we show that the largest eigenvalue of the adjacency matrix of the graph rigorously defines the epidemic threshold. The results of the model also predict the upper and lower bounds on epidemics as a function of nodal degree. The epidemic threshold is found to be a consequence of the mean field approximation. However, slow convergence to the steady-state justifies the application of the threshold concept. We used the exact 2N-state Markov chain model to explore the phase transition phenomenon for two contrasting cases, namely the line graph and the complete graph. The N-intertwined model assumes that the infection spreading over a link is a Poisson process. By introducing infection delay, we studied the influence of deviation from Poisson process assumption on epidemic threshold for the special case of a complete bi-partite graph. Due to the special structure of bi-partite graphs we were also able to derive approximate formula for the extinction probability in the first phase of the infection. In the case of SIS epidemic models, the effects of infection depend on the protection of individual nodes. We studied optimization of protection scheme for different networks. We use the results from heterogeneous N-intertwined model to determine the global optimum at the threshold. Above the threshold, the problem is a sum of ratios fractional programming problem, which is NP-complete. Therefore, we only determine the upper bound on the optimum. Contrary to the common sense, reducing the probability of infection for higher degree nodes pushes the network out of the global optimum. For the case of complete bi-partite graphs, we derive optimal threshold if only 2 fixed protection rates are available. Computer networks are generally distributed systems and protection cannot be globally optimized. The Internet is an extreme example: there is no global control center, and obtaining complete information on its global state is an illusion. To approach the issue of security over decentralized network, we derived a novel framework for network security under the presence of autonomous decision makers. The problem under the consideration is the N players non-cooperative game. We have established the existence of a Nash equilibrium point (NEP). The willingness of nodes to invest in protection depends on the price of protection. We showed that, when the price of protection is relatively high for all the nodes, the only equilibrium point is that of a completely unprotected network; while if this price is sufficiently low for a single node, it will always invest in protecting itself. We determine bounds on the Price of Anarchy (PoA), that describes how far the NEP is from the global optimum. We have also proposed two methods for steering the network equilibrium, namely by influencing the relative prices and by imposing an upper bound on infection probabilities. A quarantine is another possible measure against the epidemic. A quarantine on a set of network nodes separates them from the rest of the network by removing links. The concept of threshold and the N-intertwined model provides a tool to analyze how quarantine improves the network protection. We studied several different networks from artificially generated to real-world examples using the modularity algorithm. The real-world networks tend to show a better epidemic threshold after clustering than artificially generated graphs. The real-world networks have typically two or three big clusters and several smaller ones, while Barabasi-Albert (BA) and Erdos-Renyi (ER) graphs have several smaller clusters comparable in size. However, the number of removed links in a graph using modularity algorithm is unjustifiably high, suggesting that complete quarantine is not a viable solution for real-world networks.Network Architecture and ServicesElectrical Engineering, Mathematics and Computer Scienc

El retrato histórico en el cine actual

I Congreso Internacional de Historia y Cine: 5, 6, 7 y 8 de Septiembre de 2007

Famílias simultâneas e a dignidade da pessoa humana

The influence of the network characteristics on the virus spread is analyzed in a new-the N-intertwined Markov chain-model, whose only approximation lies in the application of mean field theory. The mean field approximation is quantified in detail. The N-intertwined model has been compared with the exact 2N-state Markov model and with previously proposed "homogeneous"or "local"models. The sharp epidemic threshold τc, which is a consequence of mean field theory, is rigorously shown to be equal to τc= (λmax (A)), where λmax (A) is the largest eigenvalue-the spectral radius-of the adjacency matrix A.A. A continued fraction expansion of the steady-state infection probability at node j is presented as well as several upper bounds. © 2008 IEEE

Pandemics and networks: The case of the Mexican flu

The recent widespread of the new Mexican flu and SARS show the high dependency on contemporary traveling patterns. The air transport network is recognized as an important channel of epidemic propagation for different diseases. In order to predict epidemic spreading and the influence of protection measures, a mathematical model of the Susceptible - Infected - Susceptible (SIS) type is used. We compare three different networks, namely the air transport network (in the USA and Europe), Erdos- Renyi (ER) graphs and complete bi-partite networks in the light of graph theoretical results based on the N-intertwined model. Using the spreading parameters of the Mexican flu estimated in Mexico City, we determine the necessary speed of countermeasures such that the epidemic is stopped. Restructuring of the air transport in the case of the USA transport network does not improve protection, while in the case of the European transport network the number of infected nodes is reduced for 10%.Network Architectures and Services (NAS)Electrical Engineering, Mathematics and Computer Scienc

Network protection against worms and cascading failures using modularity partitioning

Communication networks are prone to virus and worms spreading and cascading failures. Recently, a number of social networking worms have spread over public Web sites. Another example is error propagation in routing tables, such as in BGP tables. The immunization and error curing applied to these scenarios are not fast enough. There have been studies on the effect of isolating and curing network elements, however, the proposed strategies are limited to node removals. This paper proposes a link isolation strategy based on the quarantining of susceptible clusters in the network. This strategy aims to maximize the epidemic control while minimizing the impact on the clusters performance. We empirically study the influence of clustering on robustness against epidemics in several real-world and artificial networks. Our results show an average curing rate improvement above 50% for the studied real-world networks under analysis.Network Architectures & Services (NAS)Electrical Engineering, Mathematics and Computer Scienc

Protecting against network infections: A game theoretic perspective

Security breaches and attacks are critical problems in today’s networking. A key-point is that the security of each host depends not only on the protection strategies it chooses to adopt but also on those chosen by other hosts in the network. The spread of Internet worms and viruses is only one example. This class of problems has two aspects. First, it deals with epidemic processes, and as such calls for the employment of epidemic theory. Second, the distributed and autonomous nature of decision-making in major classes of networks (e.g., P2P, adhoc, and most notably the Internet) call for the employment of game theoretical approaches. Accordingly, we propose a unified framework that combines the N-intertwined, SIS epidemic model with a noncooperative game model. We determine the existence of a Nash equilibrium of the respective game and characterize its properties. We show that its quality, in terms of overall network security, largely depends on the underlying topology. We then provide a bound on the level of system inefficiency due to the noncooperative behavior, namely, the “price of anarchy” of the game. We observe that the price of anarchy may be prohibitively high, hence we propose a scheme for steering users towards socially efficient behavior.Network Architectures and ServicesElectrical Engineering, Mathematics and Computer Scienc

Epidemic phase transition of the SIS type in networks

Network Architectures and Services (NAS) GroupElectrical Engineering, Mathematics and Computer Scienc