How community structure aects epidemics - Clara Stegehuis

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How community structure affects epidemics Clara Stegehuis joint work with Remco van der Hofstad and Johan van Leeuwaarden Eindhoven University of Technology

Transcript of How community structure aects epidemics - Clara Stegehuis

Page 1: How community structure aects epidemics - Clara Stegehuis

How community structure affects epidemicsClara Stegehuis

joint work with Remco van der Hofstad and Johan van Leeuwaarden

Eindhoven University of Technology

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How does community structure affect epidemics?

Hierarchical configuration model

Epidemics

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Configuration model [Bollobas 1980]

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Configuration model [Bollobas 1980]

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Configuration model [Bollobas 1980]

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Configuration model [Bollobas 1980]

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Community structure is lost!

Original network Configuration model

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Hierarchical configuration model

Instead of pairing vertices, pair communities with half-edges

Start Possible result

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Easy to analyze

Use that on community level still a configuration model to obtain:

I clustering

I component sizes

I degree correlations

.........

...

· · · · · · · · ·

......

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Easy to analyze

Use that on community level still a configuration model to obtain:

I clustering

I component sizes

I degree correlations

.........

...

· · · · · · · · ·

......

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HCM*

Randomize also edges within communities

Start Possible result

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Different models

CM

Fixed degrees

HCM*

Fixed sets ofcommunities

HCM

Fixed communitystructure

Increasing randomness

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Hierarchical configuration model

Epidemics

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Bond percolation

Infect neighbor with probability p

S : fraction of vertices in largest component

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Bond percolation

Infect neighbor with probability pS : fraction of vertices in largest component

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Bond percolation results

Email network

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

edge occupation probability p

Siz

eof

larg

estc

ompo

nent

dataHCMHCM*CM

Internet router network

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

edge occupation probability pS

ize

ofla

rges

tcom

pone

nt

dataHCMHCM*CM

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SIR epidemic

Email network

0 5 10 15 20 25 300.00

0.05

0.10

0.15

time

infe

cted

node

s

dataHCMHCM*CM

Internet router network

0 5 10 15 20 25 300.00

0.10

0.20

0.30

timein

fect

edno

des

dataHCMHCM*CM

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Conclusion

I Communities are important for how epidemics spread acrossnetworks

I Exact community shapes are less important

I Communities may inhibit or enforce epidemics

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Open problem

Overlapping communities