Finding an infection source under the SIS model

We consider the problem of identifying an infection source based only on an observed set of infected nodes in a network, assuming that the infection process follows a Susceptible-Infected-Susceptible (SIS) model. We derive an estimator based on estimating the most likely infection source associated with the most likely infection path. Simulation results on regular trees suggest that our estimator performs consistently better than the minimum distance centrality based heuristic

Infection Spreading and Source Identification: A Hide and Seek Game

The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator

Identifying Infection Sources and Regions in Large Networks

Identifying the infection sources in a network, including the index cases that introduce a contagious disease into a population network, the servers that inject a computer virus into a computer network, or the individuals who started a rumor in a social network, plays a critical role in limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources and the infection regions (subsets of nodes infected by each source) in a network, based only on knowledge of which nodes are infected and their connections, and when the number of sources is unknown a priori. We derive estimators for the infection sources and their infection regions based on approximations of the infection sequences count. We prove that if there are at most two infection sources in a geometric tree, our estimator identifies the true source or sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, and when the maximum possible number of infection sources is known, we propose an algorithm with quadratic complexity to estimate the actual number and identities of the infection sources. Simulations on various kinds of networks, including tree networks, small-world networks and real world power grid networks, and tests on two real data sets are provided to verify the performance of our estimators

On the Universality of Jordan Centers for Estimating Infection Sources in Tree Networks

Finding the infection sources in a network when we only know the network topology and infected nodes, but not the rates of infection, is a challenging combinatorial problem, and it is even more difficult in practice where the underlying infection spreading model is usually unknown a priori. In this paper, we are interested in finding a source estimator that is applicable to various spreading models, including the Susceptible-Infected (SI), Susceptible-Infected-Recovered (SIR), Susceptible-Infected-Recovered-Infected (SIRI), and Susceptible-Infected-Susceptible (SIS) models. We show that under the SI, SIR and SIRI spreading models and with mild technical assumptions, the Jordan center is the infection source associated with the most likely infection path in a tree network with a single infection source. This conclusion applies for a wide range of spreading parameters, while it holds for regular trees under the SIS model with homogeneous infection and recovery rates. Since the Jordan center does not depend on the infection, recovery and reinfection rates, it can be regarded as a universal source estimator. We also consider the case where there are k>1 infection sources, generalize the Jordan center definition to a k-Jordan center set, and show that this is an optimal infection source set estimator in a tree network for the SI model. Simulation results on various general synthetic networks and real world networks suggest that Jordan center-based estimators consistently outperform the betweenness, closeness, distance, degree, eigenvector, and pagerank centrality based heuristics, even if the network is not a tree

Identifying infection sources in a network

Modern networks like social, communication and transportation networks have grown drastically in complexity. Such networks are susceptible to rapidly spreading “infection”, which can have different meanings under different contexts, including a contagious disease, a computer virus or a rumor. Timely identification of the infection sources (the index cases of a contagious disease, the original servers that injected a computer virus into the Internet, or the rumor origins that started a rumor in a social network) is critical for limiting the damage caused by the infection. The infection sources identification problem has thus attracted considerable interest from the research community over the past few years.DOCTOR OF PHILOSOPHY (EEE

Bandwidth Broker based admission control for guaranteed video quality of experience

The current Best Effort service in the Internet will not be able to cater for the increasingly demand of high bandwidth real-time video traffic. Many possible mechanisms have been proposed to provide QoS guarantees to individual traffic streams. However, the scalability and complexity issues remain unresolved. In this report, a novel Bandwidth Broker architecture based scalable QoS provisioning mechanism called Scalable MultiServ is presented. This framework provides different classes of QoS with different queuing delay bounds on top of the current Best Effort delivery service in a network so that video users may enjoy acceptable quality of experience. In our comprehensive solution, routers do not need to maintain any QoS reservation states and the computation complexity is pushed to end users, leaving simple priority scheduling at all routers, thereby addresses scalability and complexity problems. Admission Control and Domain QoS States Database system is designed for the proposed Scalable MultiServ mechanism. The performance of the architecture is evaluated by means of NS-2 simulations.Bachelor of Engineerin

Identifying infection sources in large tree networks

Estimating which nodes in a network are the infection sources, including the individuals who started a rumor in a social network, the computers that introduce a virus into a computer network, or the index cases of a contagious disease, plays a critical role in identifying the influential nodes in a network, and in some applications, limiting the damage caused by the infection through timely quarantine of the sources. We consider the problem of estimating the infection sources, based only on knowledge of the underlying network connections. We derive estimators based on approximations of the infection sequences counts. We show that if there are at most two infection sources in a geometric tree, our estimator identifies these sources with probability going to one as the number of infected nodes increases. When there are more than two infection sources, we present heuristics that have quadratic complexity. We show through simulations that our proposed estimators can correctly identify the infection sources to within a few hops with high probability

Identifying multiple infection sources in a network

Estimating which nodes are the infection sources that introduce a virus or rumor into a network, or the locations of pollutant sources, plays a critical role in limiting the potential damage to the network through timely quarantine of the sources. In this paper, we derive estimators for the infection sources and their infection regions based on the infection network geometry. We show that in a geometric tree with at most two sources, our estimator identifies these sources with probability going to one as the number of infected nodes increases. We extend and generalize our methods to general graphs, where the number of infection sources are unknown and there may be multiple sources. Numerical results are presented to verify the performance of our proposed algorithms under different types of graph structures