Distributing Antidote Using PageRank Vectors

Chung, Fan; Horn, Paul; Tsiatas, Alexander

doi:10.1080/15427951.2009.10129184

Cited by 55 publications

(38 citation statements)

References 17 publications

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“…These approaches mainly rely on nodes' locations in the overall network to determine their relative importance. Some of the popular strategies include acquaintance immunization [9], targeted immunization (e.g., [10]), page rank centrality based immunization [11] and eigenvalue centrality based immunization [12]. More recently, the problem of optimal allocation of protection resources was formulated and solved using a geometric programming approach by Preciado et al [8].…”

Section: Background and Related Workmentioning

confidence: 99%

Immunization against Infection Propagation in Heterogeneous Networks

Abbas

Bhatia

Vorobeychik

et al. 2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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Abstract-Modeling spreading processes for infections has been a widely researched area owing to its application in variety of domains especially epidemic spread and worm propagation. Until recently, infection propagation models usually inspired by epidemic spreading, solely relied upon the underlying network properties without taking into account the variation in node specific properties, such as its ability to spread infection or recover from an infection. Owing to this fact, these models have been agnostic to the effects such node heterogeneity might have in the overall infection (or immunization) process. In this paper, we incorporate node properties in a well-known Susceptible Infected Recovered Susceptible (SIRS) model for infection propagation, and propose new heuristics to curb the spread of infection in heterogeneous networks. The proposed heuristics are validated against various network topologies, including a real-world example of an email exchange network.

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Section: Background and Related Workmentioning

confidence: 99%

Immunization against Infection Propagation in Heterogeneous Networks

Abbas

Bhatia

Vorobeychik

et al. 2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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“…Literature, such as [2], has proposed the usage of PageRank and other centrality measures to stop the spread of viruses by identifying important nodes. In contrast, Preciado et al, in [7], suggest that no simple correspondence between graph organization and optimal immunization schemes exists.…”

Section: Introductionmentioning

confidence: 99%

On the Immunization of Small Computer Networks

Fagan¹

2017

SIURO

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Abstract. As complex computer networks have become integral to our life, it has become important to create effective and efficient defenses against computer viruses. To model these viruses and networks, a computer network can be treated as a graph of nodes and edges. For small graphs, we can use centrality measures to identify which nodes to immunize first. We examine an existing immunization scheme based on the computation of a bound for the decay of a virus and compare it to some proposed schemes on random graphs. We observe that an immunization scheme based on the organization of a graph can outperform a greedy scheme. We also observe that the computation of the virus decay bound does not correlate in the expected way with the ability of the virus to infect an entire network.

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“…For example, advertisers can more effectively serve niche audiences if they can identify their target communities within the larger social web, and viruses on technological or population networks can be effectively quarantined by distributing antidote to local clusters around their origins [9].…”

Section: Introductionmentioning

confidence: 99%

Finding and Visualizing Graph Clusters Using PageRank Optimization

Graham

Tsiatas

2010

Algorithms and Models for the Web-Graph

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We give algorithms for finding graph clusters and drawing graphs, highlighting local community structure within the context of a larger network. For a given graph G, we use the personalized PageRank vectors to determine a set of clusters, by optimizing the jumping parameter α subject to several cluster variance measures in order to capture the graph structure according to PageRank. We then give a graph visualization algorithm for the clusters using PageRank-based coordinates. Several drawings of real-world data are given, illustrating the partition and local community structure.

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Distributing Antidote Using PageRank Vectors

Cited by 55 publications

References 17 publications

Immunization against Infection Propagation in Heterogeneous Networks

Immunization against Infection Propagation in Heterogeneous Networks

On the Immunization of Small Computer Networks

Finding and Visualizing Graph Clusters Using PageRank Optimization

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