On Computer Viral Infection and the Effect of Immunization

Wang, Chenxi; Knight, John; Elder, Matthew

doi:10.21236/ada436727

Cited by 35 publications

(40 citation statements)

References 6 publications

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“…The problem of epidemic spread in computer networks has been studied extensively in [4,12,11,16,19,20,22]. Much of this work studies patterns of information flows and conditions under which such flows becoming epidemics.…”

Section: Related Workmentioning

confidence: 99%

On Flow Authority Discovery in Social Networks

Aggarwal¹,

Khan

Yan

2011

Proceedings of the 2011 SIAM International Conference on Data Mining

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A central characteristic of social networks is that it facilitates rapid dissemination of information between large groups of individuals. This paper will examine the problem of determination of information flow representatives, a small group of authoritative representatives to whom the dissemination of a piece of information leads to the maximum spread. Clearly, information flow is affected by a number of different structural factors such as the node degree, connectivity, intensity of information flow interaction and the global structural behavior of the underlying network. We will propose a stochastic information flow model, and use it to determine the authoritative representatives in the underlying social network. We will first design an accurate RankedReplace algorithm, and then use a Bayes probabilistic model in order to approximate the effectiveness of this algorithm with the use of a fast algorithm. We will examine the results on a number of real social network data sets, and show that the method is more effective than state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

On Flow Authority Discovery in Social Networks

Aggarwal¹,

Khan

Yan

2011

Proceedings of the 2011 SIAM International Conference on Data Mining

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“…The problem of epidemic spread in computer networks has been studied extensively in [5,12,11,13,14,15,19,20,21]. Much of this work studies patterns of information flows under which such flows becoming epidemics.…”

Section: Related Workmentioning

confidence: 99%

On Influential Node Discovery in Dynamic Social Networks

Aggarwal¹,

Lin

2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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The problem of maximizing influence spread has been widely studied in social networks, because of its tremendous number of applications in determining critical points in a social network for information dissemination. All the techniques proposed in the literature are inherently static in nature, which are designed for social networks with a fixed set of links. However, many forms of social interactions are transient in nature, with relatively short periods of interaction. Any influence spread may happen only during the period of interaction, and the probability of spread is a function of the corresponding interaction time. Furthermore, such interactions are quite fluid and evolving, as a result of which the topology of the underlying network may change rapidly, as new interactions form and others terminate. In such cases, it may be desirable to determine the influential nodes based on the dynamic interaction patterns. Alternatively, one may wish to discover the most likely starting points for a given infection pattern. We will propose methods which can be used both for optimization of information spread, as well as the backward tracing of the source of influence spread. We will present experimental results illustrating the effectiveness of our approach on a number of real data sets.

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“…While the latter result seems interesting, the authors argue that detecting nodes of high degrees in scale-free networks is a diffi cult problem. Similarly, the simulation study of Wang et al [31] examines the effects of immunization of nodes on the propagation on two topologies: rooted trees and clustered networks (composed of cliques interconnected with a small number of edges). The simulation's parameter is the propagation fan-out, the number of nodes to which the worm can send replicas at each time step.…”

Section: Sir Model and Its Variationsmentioning

confidence: 99%

“…The principal disadvantage of the studies by Wang et al [31] and Pastor-Satorras and Vespignani [23] is that immunization is static, i.e., a fraction of nodes is immunized before the worm starts propagating. In reality, the countermeasures should be dynamic in nature to play an important role in slowing down the propagation of the worm.…”

Section: Sir Model and Its Variationsmentioning

confidence: 99%

Correlation Model of Worm Propagation on Scale-Free Networks

Nikoloski¹,

Deo²,

Kučera³

2006

ComPlexUs

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The problem of network worms is worsening despite increasing efforts and expenditure on cyber-security. Worm propagation is a random process that creates a complex system of interacting agents (worm copies) over the propagation medium -a scale-free graph, representing real-world networks. Understanding the propagation of network worms on scale-free graphs is the fi rst step towards devising effective techniques for worm quarantining. After presenting the drawbacks of existing mean-fi eld models, we develop a pair-approximation (correlation) model of worm propagation that employs the salient network characteristics -order, size, degree distribution, and transitivity . Inclusion of the transitivity shows signifi cant improvement over existing pair-approximation models. The validity of the model is confi rmed by comparing the numeric solution of the model to results from our individual-based simulation. Our model demonstrates that the network structure has considerable impact on the propagation dynamics when the worm uses local propagation strategies.

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On Computer Viral Infection and the Effect of Immunization

Cited by 35 publications

References 6 publications

On Flow Authority Discovery in Social Networks

On Flow Authority Discovery in Social Networks

On Influential Node Discovery in Dynamic Social Networks

Correlation Model of Worm Propagation on Scale-Free Networks

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