The effect of network topology on the spread of epidemics

By Ayalvadi J. Ganesh, Laurent Massoulié, D. Towsley. Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., 2005

830 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7209d85ada75f3ed17b964b386f7eb242533007f

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Rumors in a Network: Who's the Culprit? (2009, background): “…we find the following surprising phase transition: asymptotically in the size of the network, the estimator finds the rumor source with probability 0 if the tree grows like a line and it finds the rumor source with probability strictly greater than 0 if the tree grows at a rate quicker than a…”
Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks (2015, background): “In [55] and [63], the following threshold conditions are provided in terms of the infection strength / . x b d where ( ) A max m is for any initial condition ( ).”
Graph Signal Processing: Overview, Challenges, and Applications (2017, methodology): “To account for the impact of the network [17], resorting to numerical studies is precluded except for very small networks since the network state space {0, 1} grows exponentially fast (2 , for N agents).”
Information source detection in the SIR model: A sample path based approach (2012, background): “Important properties of diffusion processes such as the outbreak thresholds [1] and the impact of network topologies [2] have been intensively studied.”
Graph theory and networks in Biology. (2006, background): “More recently, the problem of epidemic spread on random graph models has been studied in a mathematically rigorous fashion within the framework of Markov processes in [254].”
Optimal and scalable distribution of content updates over a mobile social network (2009, background): “The importance of edge expansion in epidemic dissemination is well known, and has recently been studied in the context of searching in peer-to-peer networks [8], the lifetime of virus infections [9], and distributed aggregation [10].”
On the approximability of influence in social networks (2008, methodology): “…study the spread of in.uence through a social network, in a model initially studied by Kempe, Kleinberg and Tardos [14, 15]: We are given a graph modeling a social network, where each node v has a (.xed) threshold tv, such that the node will adopt a new product if tv of its neighbors adopt it.”
Virus Spread in Networks (2009, background): “[9] show that, for τ < τc – a regime that is not covered by (6) –, the mean epidemic lifetime E [T ] scales as O (logN) while, for τ > τ∗ > τc where τ∗ is the generalized isoperimetric constant, E [T ] = O ¡ e a¢ , for some constant -20x10 -15 -10 -5 0”
A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks (2011, background): “This problem is important in many contexts, for example, see [13], [18], and [42].”