A 1:1000 scale model of the digital world
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Transcript of A 1:1000 scale model of the digital world
Motivation Evolution and ecology of OSNs World model Summary & outlook
Evolution of isolated online social networks is governedby underlying social structure and two dynamical processes
Online social network layer
Traditional contactnetwork layer
ActiveOnline & offline
PassiveOnline & offlineSusceptibleOnly offline
10
Motivation Evolution and ecology of OSNs World model Summary & outlook
Evolution of isolated online social networks is governedby underlying social structure and two dynamical processes
Online social network layer
Traditional contactnetwork layer
ActiveOnline & offline
PassiveOnline & offlineSusceptibleOnly offline
Mass media activation Viral activation
Deactivation Viral reactivation
Phys. Rev. X 4, 031046 10
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital ecosystem is formed by multiple networkscompeting for the attention of individuals
OSN 2
OSN 1
Underl.network
ActivePassiveSusceptible
Partial states
Virality shareDistribution
between OSNsλi = ωi(ρ
a)λ
Rich-get-richermore active
networks obtainhigher share
Here: ωi = [ρai ]σ/
∑j [ρ
aj ]σ
σ: activity affinity
Networks can coexist despite rich-get-richermechanism.
Sci. Rep. 5, 10268 • Tomorrow, 17:20, A5+A6
11
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital ecosystem is formed by multiple networkscompeting for the attention of individuals
OSN 2
OSN 1
Underl.network
ActivePassiveSusceptible
Partial states
Virality shareDistribution
between OSNsλi = ωi(ρ
a)λ
Rich-get-richermore active
networks obtainhigher share
Here: ωi = [ρai ]σ/
∑j [ρ
aj ]σ
σ: activity affinity
Networks can coexist despite rich-get-richermechanism.
Sci. Rep. 5, 10268 • Tomorrow, 17:20, A5+A6
11
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital ecosystem is formed by multiple networkscompeting for the attention of individuals
OSN 2
OSN 1
Underl.network
ActivePassiveSusceptible
Partial states
Virality shareDistribution
between OSNsλi = ωi(ρ
a)λ
Rich-get-richermore active
networks obtainhigher share
Here: ωi = [ρai ]σ/
∑j [ρ
aj ]σ
σ: activity affinity
Networks can coexist despite rich-get-richermechanism.
Sci. Rep. 5, 10268 • Tomorrow, 17:20, A5+A6
11
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital ecosystem is formed by multiple networkscompeting for the attention of individuals
OSN 2
OSN 1
Underl.network
ActivePassiveSusceptible
Partial states
Virality shareDistribution
between OSNsλi = ωi(ρ
a)λ
Rich-get-richermore active
networks obtainhigher share
Here: ωi = [ρai ]σ/
∑j [ρ
aj ]σ
σ: activity affinity
Networks can coexist despite rich-get-richermechanism.
Sci. Rep. 5, 10268 • Tomorrow, 17:20, A5+A6
11
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital ecosystem is formed by multiple networkscompeting for the attention of individuals
OSN 2
OSN 1
Underl.network
ActivePassiveSusceptible
Partial states
Virality shareDistribution
between OSNsλi = ωi(ρ
a)λ
Rich-get-richermore active
networks obtainhigher share
Here: ωi = [ρai ]σ/
∑j [ρ
aj ]σ
σ: activity affinity
Networks can coexist despite rich-get-richermechanism.
Sci. Rep. 5, 10268 • Tomorrow, 17:20, A5+A611
Motivation Evolution and ecology of OSNs World model Summary & outlook
World map of social networks: Facebook takes overand how can we explain it?
Courtesy of Vincenzo Cosenza (www.vincos.it) 13
Motivation Evolution and ecology of OSNs World model Summary & outlook
Intercountry social ties lead to an increasedintrinsic fitness of the international network
Competition Competition
Localnetwork 1
Localnetwork 2
Globalnetwork
Competition Competition
Localnetwork 1
Localnetwork 2
Globalnetwork
Globalnetwork
Frequency of intercountrysocial ties
Coarse-grainedcoupling
Effective activityinternational network moreattractive (intercountry ties)
Air travelpassengersWij proxy forintercountry social ties
ρai,int = ρai,int + α∑
j =iΩijρaj,int Ωij ∝Wij/Ni
14
Motivation Evolution and ecology of OSNs World model Summary & outlook
Intercountry social ties lead to an increasedintrinsic fitness of the international network
Competition Competition
Localnetwork 1
Localnetwork 2
Globalnetwork
Competition Competition
Localnetwork 1
Localnetwork 2
Globalnetwork
Globalnetwork
Frequency of intercountrysocial ties
Coarse-grainedcoupling
Effective activityinternational network moreattractive (intercountry ties)
Air travelpassengersWij proxy forintercountry social ties
ρai,int = ρai,int + α∑
j =iΩijρaj,int
Ωij ∝Wij/Ni
14
Motivation Evolution and ecology of OSNs World model Summary & outlook
Intercountry social ties lead to an increasedintrinsic fitness of the international network
Competition Competition
Localnetwork 1
Localnetwork 2
Globalnetwork
Competition Competition
Localnetwork 1
Localnetwork 2
Globalnetwork
Globalnetwork
Frequency of intercountrysocial ties
Coarse-grainedcoupling
Effective activityinternational network moreattractive (intercountry ties)
Air travelpassengersWij proxy forintercountry social ties
ρai,int = ρai,int + α∑
j =iΩijρaj,int Ωij ∝Wij/Ni
14
Motivation Evolution and ecology of OSNs World model Summary & outlook
Network of multi-layer networks represents globaldigital ecology for the 80 largest countries
ActivePassiveSusceptible
Partial states
Localnetwork
Globalnetwork
Effective activity
15
Motivation Evolution and ecology of OSNs World model Summary & outlook
Double meanfield approximation describes mean activitieswith global connectivity as new control parameter
x =⟨ρai,loc
⟩: mean activity of local networks
y =⟨ρai,int
⟩: mean activity of international network
x = x
[λ ⟨k⟩ xσ
xσ + (y(1 + Ω))σ[1− x]− 1
]y = y
[λ ⟨k⟩ (y(1 + Ω))σ
xσ + (y(1 + Ω))σ[1− y]− 1
]Additional control parameter Ω = α ⟨Ωij⟩ (global connectivity)
16
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks can coexist, dominate, or become extinctdepending on global connectivity and activity affinity
0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
σ
Ω
Phase diagramCoexistenceis possible
Coexistenceis impossible
Saddlenodebifurcation
Attractorswitching
Local attractsfrom
Glo
bal c
onne
ctiv
ity
Activity affinity
17
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks can coexist, dominate, or become extinctdepending on global connectivity and activity affinity
0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
σ
Ω
Phase diagramCoexistenceis possible
Coexistenceis impossible
Saddlenodebifurcation
Attractorswitching
Local attractsfrom
Glo
bal c
onne
ctiv
ity
0.0 0.2 0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
x
y
0.0 0.2 0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
x
y
0.0 0.2 0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
x
y
International winsLocal networks winNetworks coexist 17
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks can coexist, dominate, or become extinctdepending on global connectivity and activity affinity
0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
σ
Ω
Phase diagram
0.0 0.2 0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
x
y
0.2 0.4 0.6 0.8
x
0.2 0.4 0.6 0.8
x
0.2 0.4 0.6 0.8
x
Initial condition
International winsLocal networks winNetworks coexist
Coexistenceis possible
Coexistenceis impossible
Saddlenodebifurcation
Attractorswitching
Local attractsfrom
Glo
bal c
onne
ctiv
ity
17
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks can coexist, dominate, or become extinctdepending on global connectivity and activity affinity
0.4 0.6 0.80.0
0.2
0.4
0.6
0.8
σ
Ω
Phase diagramCoexistenceis possible
Coexistenceis impossible
Saddlenodebifurcation
Attractorswitching
Local attractsfrom
Glo
bal c
onne
ctiv
ity
Activity affinity
Highest probability for extinction of local networks isat intermediate value of the activity affinity σ.
17
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world is constructedwith synthetic networks for underlying social structure
Synthetic networksfor underlying societies
(S1model)
Launch timeInternational network starts
delayed except in US
Real topologyof the air travel network
Simulatefull stochastic model
18
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world is constructedwith synthetic networks for underlying social structure
Synthetic networksfor underlying societies
(S1model)
Launch timeInternational network starts
delayed except in US
Real topologyof the air travel network
Simulatefull stochastic model
18
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world is constructedwith synthetic networks for underlying social structure
Synthetic networksfor underlying societies
(S1model)
Launch timeInternational network starts
delayed except in US
Real topologyof the air travel network
Simulatefull stochastic model
18
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world is constructedwith synthetic networks for underlying social structure
Synthetic networksfor underlying societies
(S1model)
Launch timeInternational network starts
delayed except in US
Real topologyof the air travel network
Simulatefull stochastic model
18
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks always become extinct for intermediateactivity affinity but can survive otherwise
Mean prevalence of int. network: Φ =⟨
ρai,int
ρai,int+ρai,loc
⟩
19
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks always become extinct for intermediateactivity affinity but can survive otherwise
Mean prevalence of int. network: Φ =⟨
ρai,int
ρai,int+ρai,loc
⟩
19
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks always become extinct for intermediateactivity affinity but can survive otherwise
Mean prevalence of int. network: Φ =⟨
ρai,int
ρai,int+ρai,loc
⟩
19
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks always become extinct for intermediateactivity affinity but can survive otherwise
Mean prevalence of int. network: Φ =⟨
ρai,int
ρai,int+ρai,loc
⟩
19
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks always become extinct for intermediateactivity affinity but can survive otherwise
Mean prevalence of int. network: Φ =⟨
ρai,int
ρai,int+ρai,loc
⟩
19
Motivation Evolution and ecology of OSNs World model Summary & outlook
Local networks always become extinct for intermediateactivity affinity but can survive otherwise
Mean prevalence of int. network: Φ =⟨
ρai,int
ρai,int+ρai,loc
⟩
19
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world revealsconditions for survival of local networks
Intercountry tieslead to intrinsic fitness
Heterogeneous fitnesscan impede coexistence
International networkalways dominates for
intermediate activity affinity
Local networkscan survive for low and high
activity affinity
arxiv:1504.01368
20
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world revealsconditions for survival of local networks
Intercountry tieslead to intrinsic fitness
Heterogeneous fitnesscan impede coexistence
International networkalways dominates for
intermediate activity affinity
Local networkscan survive for low and high
activity affinity
arxiv:1504.01368
20
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world revealsconditions for survival of local networks
Intercountry tieslead to intrinsic fitness
Heterogeneous fitnesscan impede coexistence
International networkalways dominates for
intermediate activity affinity
Local networkscan survive for low and high
activity affinity
arxiv:1504.01368
20
Motivation Evolution and ecology of OSNs World model Summary & outlook
1:1000 scale model of the digital world revealsconditions for survival of local networks
Intercountry tieslead to intrinsic fitness
Heterogeneous fitnesscan impede coexistence
International networkalways dominates for
intermediate activity affinity
Local networkscan survive for low and high
activity affinity
arxiv:1504.01368
20
Motivation Evolution and ecology of OSNs World model Summary & outlook
Multiscale theory of the digital world: From individual tiesto globally interacting networks
Individuals Interacting Worldwide
Mod
el Strength ofsocial ties
Res
ult Weak ties
have highertransmissibility
Viral + mediaeffect & under-lying structure
Viral effect is about fourtimes stronger
Rich-get-richer& diminishingreturns
Coexistance of amoderate numberof services
Network of net-works & effectiveactivity
Local networks canprevail under certainconditions
Focu
s
12
3
101 - 102 105 - 106 106 - 109 >109
Ord
er
Isolatednetwork networks
PRX 4, 031046 Sci. Rep. 5, 10268 arxiv:1504.01368 22
Motivation Evolution and ecology of OSNs World model Summary & outlook
Multiscale theory of the digital world: From individual tiesto globally interacting networks
Individuals Interacting Worldwide
Mod
el Strength ofsocial ties
Res
ult Weak ties
have highertransmissibility
Viral + mediaeffect & under-lying structure
Viral effect is about fourtimes stronger
Rich-get-richer& diminishingreturns
Coexistance of amoderate numberof services
Network of net-works & effectiveactivity
Local networks canprevail under certainconditions
Focu
s
12
3
101 - 102 105 - 106 106 - 109 >109
Ord
er
Isolatednetwork networks
PRX 4, 031046 Sci. Rep. 5, 10268 arxiv:1504.01368 22
Motivation Evolution and ecology of OSNs World model Summary & outlook
Multiscale theory of the digital world: From individual tiesto globally interacting networks
Individuals Interacting Worldwide
Mod
el Strength ofsocial ties
Res
ult Weak ties
have highertransmissibility
Viral + mediaeffect & under-lying structure
Viral effect is about fourtimes stronger
Rich-get-richer& diminishingreturns
Coexistance of amoderate numberof services
Network of net-works & effectiveactivity
Local networks canprevail under certainconditions
Focu
s
12
3
101 - 102 105 - 106 106 - 109 >109
Ord
er
Isolatednetwork networks
PRX 4, 031046 Sci. Rep. 5, 10268 arxiv:1504.01368 22
Motivation Evolution and ecology of OSNs World model Summary & outlook
Multiscale theory of the digital world: From individual tiesto globally interacting networks
Individuals Interacting Worldwide
Mod
el Strength ofsocial ties
Res
ult Weak ties
have highertransmissibility
Viral + mediaeffect & under-lying structure
Viral effect is about fourtimes stronger
Rich-get-richer& diminishingreturns
Coexistance of amoderate numberof services
Network of net-works & effectiveactivity
Local networks canprevail under certainconditions
Focu
s
12
3
101 - 102 105 - 106 106 - 109 >109
Ord
er
Isolatednetwork networks
PRX 4, 031046 Sci. Rep. 5, 10268 arxiv:1504.01368 22
Just as a monopoly in economy is a threat to free markets, the lack of
poses a threat to the digital diversity
freedom of information.
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital diversity is important. So write downthe references and contact information now!
References:
K.-K. Kleineberg, M. Boguña.PRX 4, 031046, 2014
K.-K. Kleineberg, M. Boguña.Sci. Rep. 5, 10268, 2015 (tomorrow, 17:20, A5+A6)
K.-K. Kleineberg, M. Boguña.arxiv:1504.01368, 2015
Kaj Kolja Kleineberg:
• @KoljaKleineberg
in • Kaj Kolja Kleineberg24
Motivation Evolution and ecology of OSNs World model Summary & outlook
Digital diversity is important. So write downthe references and contact information now!
References:
K.-K. Kleineberg, M. Boguña.PRX 4, 031046, 2014
K.-K. Kleineberg, M. Boguña.Sci. Rep. 5, 10268, 2015 (tomorrow, 17:20, A5+A6)
K.-K. Kleineberg, M. Boguña.arxiv:1504.01368, 2015
Kaj Kolja Kleineberg:
• @KoljaKleineberg← Slides!
in • Kaj Kolja Kleineberg24
Motivation Evolution and ecology of OSNs World model Summary & outlook
CREDITS
Social media chalk: mkhmarketing.wordpress.comObsolete hardware David Haywardoil field: Damian GadalCat attention: David CornejoCables: jerry johnNetwork "ring": Adam BeasleyBoxing gloves: Gabriele FumeroWorld: Lorenzo BaldiniMegaphone: Alex Auda SamoraBiohazard: Shailendra ChouhanLayer icon: MentaltoyBalance (scale) icon: Roman KovbasyukDeath symbol: Mila Redko
Pie Chart: P.J. OnoriMoney sack: Lemon LiuTeam icon: Joshua JonesHand icon: irene hoffmanarm with muscle: Sergey KrivoyTime: Richard de VosNo: P.J. OnoriLocal: Phil GoodwinSummary (article) icon: Stefan Parnarovflower: Nishanth JoisRead magazine: Evan TravelsteadGlobe 2: Ealancheliyan sdices: Drew Ellis
Icons: thenounproject.com
Kaj Kolja Kleineberg:
• @KoljaKleineberg
in • Kaj Kolja Kleineberg25