The power of epidemics

Vogels, Werner; Renesse, Robbert van; Birman, Kenneth P.

doi:10.1145/774763.774784

Cited by 71 publications

(8 citation statements)

References 4 publications

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“…Rumour-like mechanisms form the basis for the phenomena of viral marketing, where companies exploit social networks of their customers on the Internet in order to promote their products via the socalled 'word-of-email' and 'word-of-web' [4]. Finally, rumor-mongering forms the basis for an important class of communication protocols, called gossip algorithms, which are used for large-scale information dissemination on the Internet, and in peer-to-peer file sharing applications [5,6].…”

Section: Introductionmentioning

confidence: 99%

Theory of rumour spreading in complex social networks

Nekovee

Moreno

Bianconi

et al. 2007

Physica A: Statistical Mechanics and its Applications

665

441

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We introduce a general stochastic model for the spread of rumours, and derive meanfield equations that describe the dynamics of the model on complex social networks (in particular those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behavior and dynamics of the model on several models of such networks: random graphs, uncorrelated scalefree networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

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Section: Introductionmentioning

confidence: 99%

Theory of rumour spreading in complex social networks

Nekovee

Moreno

Bianconi

et al. 2007

Physica A: Statistical Mechanics and its Applications

665

441

View full text Add to dashboard Cite

show abstract

“…For example, in [29,57] messages are forwarded according to a set of predefined probabilistic rules, whereas [53] discusses a deterministic algorithm, approximating the connected dominating set of each node. Alternative information propagation techniques simulate various epidemic models [25,55,56,63].…”

Section: Related Workmentioning

confidence: 99%

“…Epidemic Algorithms. An alternative approach to be mentioned in this scope is the use of epidemic algorithms [9,55,63]. There exist a variety of epidemic algorithms, starting with the basic epidemic protocol [18,28], through neighborhood epidemics [25] and up to hierarchical epidemics [56].…”

Section: Related Workmentioning

confidence: 99%

TTLed Random Walks for Collaborative Monitoring in Mobile and Social Networks

Altshuler

Elovici

2011

Handbook of Optimization in Complex Networks

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Complex network and complex systems research has been proven to have great implications in practice in many scopes including Social Networks, Biology, Disease Propagation, and Information Security. One can use complex network theory to optimize resource locations and optimize actions. Randomly constructed graphs and probabilistic arguments lead to important conclusions with a possible great social and financial influence. Security in online social networks has recently become a major issue for network designers and operators. Being "open" in their nature and offering users the ability to compose and share information, such networks may involuntarily be used as an infection platform by viruses and other kinds of malicious software. This is specifically true for mobile social networks, that allow their users to download millions of applications created by various individual programmers, some of which may be malicious or flawed. In order to detect that an application is malicious, monitoring its operation in a real environment for a significant period of time is often required. As the computation and power resources of mobile devices are very limited, a single device can monitor only a limited number of potentially malicious applications locally. In this work, we propose an efficient collaborative monitoring scheme that harnesses the collective resources of many mobile devices, generating a "vaccination"-like effect in the network. We suggest a new local information flooding algorithm called agation (TPP). The algorithm is implemented in any mobile device, periodically monitors one or more applications and reports its conclusions to a small number of other mobile devices, who then propagate this information onwards, whereas each message has a predefined "Time-to-Live" (TTL) counter. The algorithm is analyzed, and is shown to outperform the existing state of the art information propagation algorithms, in terms of convergence time as well as network overhead. We then show both analytically and experimentally that implementing the proposed algorithm significantly reduces the number of infected mobile devices. Finally, we analytically prove that the algorithm is tolerant to the presence of adversarial agents that inject false information into the system.

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“…For communicating the tasks and termination messages across the network, an epidemic-style or gossiping protocol (see, e.g., Ref. [16,17]) can be utilised. In principle, this is similar to the spreading of good solutions: each agent forwards the task initiation/termination messages it receives to its neighbours (or a subset of those), until some criteria is met (e.g., a large enough number of neighbours have already received the message).…”

Section: Scheduling: Task Initiation and Terminationmentioning

confidence: 99%

A Method for Decentralised Optimisation in Networks

Saramäki¹

2005

AIP Conference Proceedings

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We outline a method for distributed Monte Carlo optimisation of computational problems in networks of agents, such as peer-to-peer networks of computers. The optimisation and messaging procedures are inspired by gossip protocols and epidemic data dissemination, and are decentralised, i.e. no central overseer is required. In the outlined method, each agent follows simple local rules and seeks for better solutions to the optimisation problem by Monte Carlo trials, as well as by querying other agents in its local neighbourhood. With proper network topology, good solutions spread rapidly through the network for further improvement. Furthermore, the system retains its functionality even in realistic settings where agents are randomly switched on and off.

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The power of epidemics

Cited by 71 publications

References 4 publications

Theory of rumour spreading in complex social networks

Theory of rumour spreading in complex social networks

TTLed Random Walks for Collaborative Monitoring in Mobile and Social Networks

A Method for Decentralised Optimisation in Networks

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