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Epidemics and information spreading
Leonid E. Zhukov
School of Data Analysis and Artificial IntelligenceDepartment of Computer Science
National Research University Higher School of Economics
Social Network AnalysisMAGoLEGO course
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Lecture outline
1 EpidemicsSI modelSIS modelSIR model
2 Information spread
3 Propagation trees
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Flu contagion
Infected - red, friends of infected - yellowN. Christakis, J. Fowler, 2010
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Global contagion
Simulated epidemic:gray lines - passenger flow, red symbols epidemics location
D. Brockmann, D. Helbing, 2013
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Network epidemic model
Given a network G of potential contacts
Three states model: susceptible, infected, recovered
Probabilistic model (state of a node):si (t) - probability that at t node i is susceptiblexi (t) - probability that at t node i is infectedri (t) - probability that at t node i is recovered
β - infection rate (probably to get infected on a contact in time δt)γ - recovery rate (probability to recover in a unit time δt)
connected component - all nodes reachable
network is undirected (matrix A is symmetric)
if graph complete - fully mixing model
Based upon models from mathematical epidemiology, W.O. Kermackand McKendrick, 1927
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Probabilistic model
Two processes:
Node infection:
Pinf ≈ βsi (t)∑
j∈N (i)
xj(t)δt
Node recovery:
Prec = γxi (t)δt
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SI model
SI ModelS −→ I
Probabilities that node i : si (t) - susceptible, xi (t) -infected at t
xi (t) + si (t) = 1
β - infection rate, probability to get infected in a unit time
xi (t + δt) = xi (t) + βsi∑j
Aijxjδt
infection equations
dxi (t)
dt= βsi (t)
∑j
Aijxj(t)
xi (t) + si (t) = 1
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SI simulation
1 Every node at any time step is in one state {S , I}2 Initialize c nodes in state I
3 On each time step each I node has a probability β to infect itsnearest neighbors (NN), S → I
Model dynamics:
I + Sβ−→ 2I
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SI model
β = 0.5
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SI model
β = 0.5
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SI model
β = 0.5
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SI model
β = 0.5
Leonid E. Zhukov (HSE) Lecture 7 26.05.2017 9 / 36
SI model
β = 0.5
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SI model
β = 0.5
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SI model
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SI model
1 growth rate of infections depends on λ1
2 All nodes in connected component get infected t →∞ xi (t)→ 1
3 probability of infection of nodes depends on v1 , i.e v1i
image from M. Newman, 2010
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SIS model
SIS ModelS −→ I −→ S
Probabilities that node i : si (t) - susceptible, xi (t) -infected at t
xi (t) + si (t) = 1
β - infection rate, γ - recovery rate
infection equations:
dxi (t)
dt= βsi (t)
∑j
Aijxj(t)− γxi
xi (t) + si (t) = 1
Model dynamics: {I + S
β−→ 2I
Iγ−→ S
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SIS simulation
1 Every node at any time step is in one state {S , I}2 Initialize c nodes in state I
3 Each node stays infected τγ =∫∞
0 τe−τγdτ = 1/γ time steps
4 On each time step each I node has a probability β to infect itsnearest neighbors (NN), S → I
5 After τγ time steps node recovers, I → S
Model dynamics: {I + S
β−→ 2I
Iγ−→ S
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SIS model
β = 0.5, τ = 2
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SIS model
β = 0.5, τ = 2
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SIS model
β = 0.5, τ = 2
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SIS model
β = 0.5, τ = 2
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SIS model
β = 0.5, τ = 2
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SIS model
β = 0.5, τ = 2
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SIS model
β = 0.5, τ = 2
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SIS model
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SIS model
β = 0.2, τ = 2
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SIS model
β = 0.2, τ = 2
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SIS model
β = 0.2, τ = 2
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SIS model
β = 0.2, τ = 2
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SIS model
β = 0.2, τ = 2
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SIS model
β = 0.2, τ = 2
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SIS model
β = 0.2, τ = 2
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SIS model
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SIS model
Epidemic threshold R0:
if βγ < R0 - infection dies over time
if βγ > R0 - infection survives and becomes epidemic
In SIS model: R0 = 1λ1, Av1 = λ1v1
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SIR model
SIR ModelS −→ I −→ R
probabilities si (t) -susceptable , xi (t) - infected, ri (t) - recovered
si (t) + xi (t) + ri (t) = 1
β - infection rate, γ - recovery rate
Infection equation:
dxidt
= βsi∑j
Aijxj − γxi
dridt
= γxi
xi (t) + si (t) + ri (t) = 1
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SIR simulation
1 Every node at any time step is in one state {S , I ,R}2 Initialize c nodes in state I
3 Each node stays infected τγ = 1/γ time steps
4 On each time step each I node has a prabability β to infect itsnearest neighbours (NN), S → I
5 After τγ time steps node recovers, I → R
6 Nodes R do not participate in further infection propagation
Model dynamics: {I + S
β−→ 2I
Iγ−→ R
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SIR model
β = 0.5, τ = 2
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SIR model
β = 0.5, τ = 2
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SIR model
β = 0.5, τ = 2
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SIR model
β = 0.5, τ = 2
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SIR model
β = 0.5, τ = 2
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SIR model
β = 0.5, τ = 2
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SIR model
β = 0.5, τ = 2
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SIR model
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SIR model
β = 0.2, τ = 2
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SIR model
β = 0.2, τ = 2
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SIR model
β = 0.2, τ = 2
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SIR model
β = 0.2, τ = 2
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SIR model
β = 0.2, τ = 2
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SIR model
β = 0.2, τ = 2
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SIR model
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SIR model
Epidemic threshold R0:βγ > R0 - infection survives and becomes epidemic
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SIR model
Epidemic threshold R0:βγ < R0 - infection dies over time
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5 Networks, SIR
Networks: 1) random, 2) lattice, 3) small world, 4) spatial, 5) scale-free
image from Keeling et al, 2005
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5 Networks, SIR
Networks: 1) random, 2) lattice, 3) small world, 4) spatial, 5) scale-free
Keeling et al, 2005
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Effective distance
J. Manitz, et.al. 2014, D. Brockman, D. Helbing, 2013
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Social contagion
Social contagion phenomena refer to various processes that depend on theindividual propensity to adopt and diffuse knowledge, ideas, information.
Similar to epidemiological models:- ”susceptible” - an individual who has not learned new information- ”infected” - the spreader of the information- ”recovered” - aware of information, but no longer spreading it
Two main questions:- if the rumor reaches high number of individuals- rate of infection spread
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Branching process
Simple contagion model:- each infected person meets k new people- independently transmits infection with probability p
image from David Easley, Jon Kleinberg, 2010
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Branching process
Galton-Watson branching random process, R0 = pk
if R0 = 1, the mean of number of infected nodes does not change
if R0 > 1, the mean grows geometrically as Rn0
if R0 < 1, the mean shrinks geometrically as Rn0
R0 = pk = 1 - point of phase transition
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Propagation tree
Rumors/news/infection spreads over the edges: propagation tree (graph)
Sometimes called cascade
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Online newsletter experiment
Online email newsletter + reward for recommending it
7,000 seed, 30,000 total recipients, around 7,000 cascades, size form2 nodes to 146 nodes, max depth 8 steps
on average k = 2.96, infection probability p = 0.00879
Suprisingly: no loops, triangles, closed paths
Iribarren et.al, 2009
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Mem diffusion
Mem diffusion on Twitter
L. Weng et.al, 2012
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References
Epidemic outbreaks in complex heterogeneous networks. Y. Moreno,R. Pastor-Satorras, and A. Vespignani. Eur. Phys. J. B 26, 521-529,2002.
Networks and Epidemics Models. Matt. J. Keeling and Ken.T.D.Eames, J. R. Soc. Interfac, 2, 295-307, 2005
Simulations of infections diseases on networks. G. Witten and G.Poulter. Computers in Biology and Medicine, Vol 37, No. 2, pp195-205, 2007
Dynamical processes on complex networks. A. Barrat, M. Barthelemy,A. Vespignani Eds., Cambridge University Press 2008
Dynamics of rumor spreading in complex networks. Y. Moreno, M.Nekovee, A. Pacheco, Phys. Rev. E 69, 066130, 2004
Theory of rumor spreading in complex social networks. M. Nekovee,Y. Moreno, G. Biaconi, M. Marsili, Physica A 374, pp 457-470, 2007.
Impact of Human Activity Patterns on the Dynamics of InformationDiffusion. J.L. Iribarren, E. Moro, Phys.Rev.Lett. 103, 038702, 2009.
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