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Dynamics of Social Interactions at Short Timescales
G. Bianconi Department of Physics, Northeastern University
SAMSI Workshop: Dynamics of networksSAMSI, January 10-12, 2011
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Complex networks
describe
the underlying structure of interacting complex
Biological, Social and Technological systems.
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Dynamics on networks
Scale-free degree distribution change the critical behavior of the
Ising model, Percolation,
disease spreading
Spectral propertiesof the Laplacian matrix change the
synchronization properties
of networks with complex topologiesNishikawa et al.PRL 2003
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How do critical phenomenaon complex networks change if we include
the spatial interactions?
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Annealed uncorrelatedcomplex networks
In annealed uncorrelated complex networks, we assign to each node an expected degree
Each link is present with probability pij
The degree ki a node i is a Poisson variable with mean i
€
pij =θ iθ j
θ N
€
= k
θ 2 = k(k −1)
Boguna, Pastor-Satorras PRE 2003
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Ising model in annealedcomplex networks
The Ising model on annealed complex networks has Hamiltonian given by
The critical temperature is given by
The magnetization is non-homogeneous€
Tc = Jθ 2
θ= J
k(k −1)
k
€
si = tanh β θ iJS + hi( )[ ]
€
H = −J
2 θ Nsiθ iθ js j − hisi
i
∑i≠ j
∑
G. Bianconi 2002,S.N. Dorogovtsev et al. 2002, Leone et al. 2002, Goltsev et al. 2003,Lee et al. 2009
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Critical exponents of the Ising model on complex topologies
M C(T<Tc)
>5 |Tc-T|1/2 Jump at Tc |Tc-T|-1
=5 |T-Tc|1/2/(|ln|TcT||)1/2 1/ln|Tc-T| |Tc-T|-1
3<<5 |Tc-T|1/( |Tc-T| |Tc-T|-1
=3 e-2T/ T2e-4T/ T-1
2<<3 T T T-1
But the critical fluctuations still remain mean-field !
€
P(k) ∝ k −γ
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Ensembles of spatial complex networks
The function J(d) can be measured in real spatial
networks
€
pij =θ iθ jJ(
r r i,
r r j )
1+θ iθ jJ(r r i,
r r j )
≅θ iθ jJ(r r i,
r r j )
QuickTime™ and a decompressor
are needed to see this picture.
The maximally entropic network with spatial structure has link probability given by
Airport Network Bianconi et al. PNAS 2009
J(d)
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Annealead Ising model in spatial complex networks The linking probability of spatial complex networks is
chosen to be
The Ising model on spatial annealed complex networks has Hamiltonian given by
We want to study the critical fluctuations in this model as a function of the typical range of the interactions
€
pij = θ iθ jJ(r r i,
r r j )
€
H( si{ }) = −1
2siθ iJijθ js j − H isi
i
∑i≠ j
∑
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Stability of the mean-field approximation
The partition function is given by
The magnetization in the mean field approximation is given by
The susceptibility is then evaluated by stationary phase approximation €
mi0 = tanh β(H i + θ iJijθ jm j
0
j
∑ ) ⎡
⎣ ⎢ ⎢
⎤
⎦ ⎥ ⎥€
Z = e−βH si{ }( )
si{ }
∑
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Dynamics on spatial networks:the Ising model
We assume that the spectrum is given by
is the spectral gap and c the spectral edge.
Anomalous critical fluctuations sets in only if the gap vanish in the thermodynamic limit, and S<1
For regular lattice S =(d-2)/2 S<1 only if d<4 The effective dimension of complex networks is deff =2S +2
€
ρ(λ ) ∝ (λ c − λ )δ S
Δ = Λ − λ c
ρ
c
(S. Bradde, F. Caccioli, L. Dall’Asta and G. Bianconi PRL 2010)
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Dynamics of networks
At any given time dynamical networks looks disconnected
Protein complexes during the cell cycle of yeast
Social networks (phone calls, small
gathering of people)
De Lichtenberg et al.2005
Barrat et al.2008
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Human social interaction Human social interaction
are characterized by are characterized by networks networks
at different level of at different level of organizationorganization
Friendships Cities Political parties
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Human social interactionsare organized at different
time scales• From long lasting friendships
and collaborations
• To the duration of a single phone-call or the duration of a small gathering during the coffee break of a conference
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Bursty human activities
Einstein and Darwin correspondence
Olivera and Barabasi Nature (2005)
Human dynamics is not described by Poisson processes
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Inter-event time of human activities
Vazquez et al. PRE (2006)
€
N(τ ) ∝ τ −α
α ≈1
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Queuing model for bursty human activities
Priorities and random activities
Queuing model(Barabasi Nature 2005,Vazquez PRL 2005)
€
P(τ ) ∝1
τe−τ /τ 0
€
P(τ ) ∝1
τ
Only priorities
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New data: face-to-face interactions
Bluetooth sensors (Infocom 2005 conference)IMOTE data set
-Temporal resolution 120sMIT experiment
100 students for 9 months-Time resolution 300s
Radio Frequence Identification Devices (RFID)
-face-to face interactions at a distance of 1-2 meters- temporal resolution of 20s
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Distribution of contact lifetimes and intercontact duration
Infocom 2005 conference41 sensor for 3 daysSampling period 120s
100 MIT students for 9 monthsSampling period 300s
Chaintreaux et al. 2005
Eagle and Pentland Reality Mining 2006
αττ −∝> )( tP Contact Intercontact
IMOTE
MIT
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Duration of contacts
A. Barrat, C. Cattuto, V. Colizza, J.F. Pinton,W. Van den Broeck, A. Vespignani Arxiv:0811.4170
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Weighted social network
A. Barrat, C. Cattuto, V. Colizza, J.F. Pinton,W. Van den Broeck, A. Vespignani Arxiv:0811.4170
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Cognitive Hebbian mechanismsReinforcement dynamics in
social interactions
For the interacting individual
The longer an individual interacts with a group the less is likely to leave the group
For the isolated individual
The longer and individual is isolated the less is likely to interact with a group
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Stochastic processes with reinforcement
Polya urnsReinforced random walkHebbian LearningReplicator DynamicsChinese restaurant processesPreferential attachment in networks
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The ingredients of the dynamical pairwise model
The individual i is associated to a state ni=0,1 indicating if he/she is isolated or interacting to a time ti which is the last time it has changed his state
Reinforcement dynamicsThe more an individual is in a state the less likely it that he/she
change his/her state
Transition rates Only between 0 1 1 0
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The dynamical paiwise model
Choose one random agent
If n=0 with probability p0(t,ti)he connects to another isolatedagent chosen with probability p0(t,ti)
If n=1, with probability p1(t,ti)there is a transitionand he/she disconnects from his/her group
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Choice of pn(t,ti)
Absence of reinforcement
Presence of reinforcement
€
p0,1(t, ti) =b0,1
τ +1( )
τ =t − ti
N
€
p0,1(t, ti) = b0,1
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The dynamical equationswith reinforcement
The dynamical equations for
the number of individuals N0,1(t,ti) that at time t
are in state 0,1 since time ti are given by
€
€
∂N0(t, ti)
∂t= −2
b0
(τ +1)N0(t, ti) + π10(ti)δ t ,ti
∂N1(t, ti)
∂τ= −2
b1
(τ +1)N1(t, ti) + π 01(ti)δ t ,ti
τ =t − ti
N
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Structure of the dynamical solution
€
N0(t, t ') = π10(t ') 1+τ( )−2b0
N1(t, t ') = π 01(t ') 1+τ( )−2b1
€
π10(t) =2
Np1(t, t ')N1(t, t')
t '
∑
π 01(t) =2
Np0(t, t ')N0(t, t')
t '
∑
Where the transition rates are given in terms of N0,1(t,t’)
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Self-consistent assumptionand phase diagram
€
πm,n (t) = ˜ π m,n
t
N
⎛
⎝ ⎜
⎞
⎠ ⎟−α
€
α =0
α = max(1 − 2b0,1− 2b1)
Stationary phase (white)
Non-stationary phase
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Transition rates:simulation vs. analytical
results
Green
Stationary region
Red
b0<0.5,b1>0.5
Blue
b0<0.5, b1<0.5
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Contact and inter-contact time distributions
Stationary region Non-stationary region (b1<0.5 b0<0.5)
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The dynamical model with groups of any size
The individual i is associated to a state ni=0,1,2… indicating the number of other individual
in his/her group to a time ti which is the last time it has changed his state
Reinforcement dynamicsThe more an individual is in a state the less likely it that he/she
change his/her state
Transition rates Only between n n+1 or n n-1 An individual in a group which is changing state can either detach himself/herself from his/her group with rate or
introduce an insolated individual to its group with rate 1-
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The dynamical model
Choose one random agentIf n=0 with probability p0(t,ti)he connects to another isolatedagent chosen with probability p0(t,ti)
If n>0, with probability pn(t,ti)there is a transition-with probability he/she connects to an insolated agent chosen with probability p0(t,ti)-with probability 1- he/she disconnects from his/her group
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Choice of pn(t,ti)
In presence of reinforcement
€
pn (t, ti) =bn
τ +1( )
bn = b1 for n ≥1
τ =t − ti
N
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The dynamical equationswith reinforcement
The dynamical equations for the number of individuals Nn(t,ti) that at time t are in state n since time ti are given by
with (t) given by
€
€
∂N0(t, ti)
∂t= −
b0
(τ +1)2 + (1− λ )β[ ]N0(t, ti) + π 0(ti)δ t,t i
∂Nn (t, ti)
∂τ= −(n +1)
b1
(τ +1)Nn (t, ti) + π n (ti)δ t ,ti
n ≥1
€
=Nn (ti,τ )
b1
(τ +1)dτ∫
n≥1
∑
N0(ti,τ )b0
(τ +1)dτ∫
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Phase diagram of the model
(I) Stationary region
(II) Non-stationary region
(III) Self-consistent assumption breaks-down
€
πm (t) = ˜ π mt
N
⎛
⎝ ⎜
⎞
⎠ ⎟−α
€
α =0
€
α =max 1 − 2b1,1 − b0
3λ −1
2λ −1
⎛
⎝ ⎜
⎞
⎠ ⎟
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Lifetime of a group of size n+1
€
N0(τ ) ∝ (τ +1)−b0 (2+(1−λ )α )
N1(τ ) ∝ (τ +1)−2b1
N i(τ ) ∝ (τ +1)−b1 ( i+1)
Langer groups are more unstable
(J. Stehle, A. Barrat and G. Bianconi PRE 2010)
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Real data versus model
The model well capture the distribution of lifetime of different group sizes of small human gatherings
(Sociopatterns,data from Berlin conference )
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Instability for the formation of a large group in region I
€
n ∝ (λ − λ c )−1
λ c = 0.5
The model present an instability for the formation of a large group of the order of magnitude of N
<n>
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Strong finite size effects in region III
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Features of the nodesFeatures of the nodes
In complex networks nodes are generally heterogeneous
and they are characterized by specific features
In Social networks nodes have specific features: age, gender, type of jobs, drinking and smoking habits,
nationality
Specific feature might affects the social inclination of different people, therefore a natural first generalization of the model would describe heterogeneous social behavior
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Heterogeneous model
The agents are assigned a parameter i
drawn from a uniform distribution in (0,1)
that describe their social behavior and we call sociability
The larger is the more social is the agent behavior
€
p0(η i, t, ti) =η i
τ +1( )
pn (η i, t, ti) =1 −η i
τ +1( )for n ≥1
τ =t − ti
N
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Pairwise heterogeneous model:
The duration of contacts of agents with sociability
€
N0(η, t, ti) = π10η (t ') 1+
t − t '
N
⎛
⎝ ⎜
⎞
⎠ ⎟−2η
N1(η,η ', t, ti) = π 01η ,η '(t') 1+
t − t'
N
⎛
⎝ ⎜
⎞
⎠ ⎟−2+η +η '
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Self-consistent solution
€
π10η (t) = ˜ π 10
η t
N
⎛
⎝ ⎜
⎞
⎠ ⎟−α (η )
π 01η ,η '(t) = ˜ π 01
η ,η ' t
N
⎛
⎝ ⎜
⎞
⎠ ⎟−α (η ,η ' )
€
α(η) = max(1− 2η,η −1/2)
α (η,η ') = α (η) + α (η ')
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Aggregated data for the pairwise heterogeneous model:
simulations versus analytical results
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Heterogeneous model with groups of any size
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Conclusions
Human social interaction on a fast timescales are characterized by a dynamics with reinforcement that is able to predict both power-law distribution of durations of contacts and inter-contact times.
The model show a rich phase diagram with the power-law lifetime of groups persisting also in the non-stationary region
The model can be easily generalized to include for heterogeneous sociability of the agents
The model is a perfect platform to perform simulation of social behavior on the fast time scale
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Many thanks go to my collaborators
Kun Zhao
(Northeastern University, USA)
Alain Barrat, Juliette Stehle’
(Universite’ de Marseille, France,SOCIOPATTERNS)
Ciro Cattuto, Wouter Van den Broeck, Jean-Francois Pinton
(ISI Foudation,Tourin, SOCIOPATTERNS)