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Talk given at the data driven dynamical networks workshop, Les Houches, France, http://bit.ly/bUEf8n .
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What’s in a crowd? Analysis of face-to-facebehavioral networks
Lorenzo Isella1, Alain Barrat1,2, Juliette Stehlé2,Jean-François Pinton3, Wouter Van den Broeck1 and
Ciro Cattuto1
1Complex Networks and Systems Group, Institute for Scientific Interchange (ISI)Foundation, Turin, Italy.
2Centre de Physique Théorique, CNRS UMR 6207, Marseille, France.3Laboratoire de Physique de l’ENS Lyon, CNRS UMR 5672, Lyon, France.
Workshop on data driven dynamical networks, LesHouches, France, 2010
Outline
Overview of the RFID infrastructure deployed to mine forface-to-face proximity⇒ networks of human interactions.Network structural analysis.Network resilence.Information spreading: longitudinal network⇐⇒ causality
reachability and variabilitykinetics of information spreading
Conclusions.
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Goals and Case Studies
Deployment of wearable RFID devices to collect dataabout human interaction in different social environments.Focus on two main case studies
Science Gallery (SG) at the Trinity College, Dublin, Ireland(∼ 3 months, ∼ 10000 visitors).HT09 conference, Turin, Italy (3 days, ∼ 100 participants).
Patented technology and data from Sociopatterns Project(http://www.sociopatterns.org/).Applications in computer science (ubiquitous computing,P2P) and computational epidemiology (causality,non-homogeneous mixing).
Overview of the InfrastructureTags exchange packets at various powers and report theircontacts to antennas broadcasting the data to a server.Low-power packets expose face-to-face interactions atsmall distances (∼ 1m).
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
From Physical Proximity to Networks
Natural representation of physical proximity as a network inaddition to
scalabilityunobtrusivenesslow costhigh spatial resolution ∼ 1 meterhigh temporal resolution ∼ 5− 20 seconds.
Aggregated NetworksAggregate all the contacts along 24 hours.
HT09: June, 30th
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SG: July, 14th
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SG: May, 19th
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SG: May, 20th
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Human Dynamics and Network Topology 1/2
Entanglement of human behavior and network topology.
Visit duration (min)
P(visitduration
)
0.000
0.002
0.004
0.006
0.008
101 102
12:00 to 13:0013:00 to 14:0014:00 to 15:0015:00 to 16:0016:00 to 17:0017:00 to 18:0018:00 to 19:0019:00 to 20:00
Human Dynamics and Network Topology 2/2Short-tailed P(k) and broad P(wij) and P(∆tij).
SG
k
P(k)
10-5
10-4
10-3
10-2
10-1
0 10 20 30 40 50 60 70
HT09
k
P(k)
10-4
10-3
10-2
10-1
0 20 40 60 80
∆tij (sec)
P(∆
t ij)
10-6
10-5
10-4
10-3
10-2
10-1
100
101 102 103 104
SGHT09
wij (sec)
P(w
ij)
10-5
10-4
10-3
10-2
10-1
100
101 102 103 104
SGHT09
Random Networks and SmallworldnessNetwork topology↔ information spreading.
HT09: June, 30th (rewired)
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SG: July, 14th (rewired)●
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HT09: June, 30th
l
M(l)/M
(∞)
0.2
0.4
0.6
0.8
1.0
1 2 3 4
Aggregated networkRewired networks
SG: July, 14th
l
M(l)/M
(∞)
0.0
0.2
0.4
0.6
0.8
1.0
1 2 3 4 5 6 7 8 9 10
Aggregated networkRewired networks
Dismantling strategies 1/2Removal strategies expose network structure.Cumulative duration and/or sophisticated measures(Onnela et al., PNAS,104, 7332 (2007)), similarity, etc..
i jji
i j ji
Oij = 0 Oij = 1/3
Oij = 1Oij = 2/3
Dismantling strategies 2/2Topology-based strategies enhance networkfragmentation.Removing strong links as least effective strategy.
HT2009: June, 30th
Removal Fraction
N1
0
20
40
60
80
100
0.0 0.2 0.4 0.6 0.8 1.0
increasing wij
decreasing wij
increasing Oij
increasing simij
Dublin: July, 14th
Removal Fraction
N1
0
50
100
150
200
250
300
0.0 0.2 0.4 0.6 0.8 1.0
increasing wij
decreasing wij
increasing Oij
increasing simij
Deterministic SI model 1/2
SI model S + I → 2I, infection probability ε.Set ε = 1: snowball deterministic model (avoidstochasticity).Beyond epidemiology: paradigm for information diffusionand causality on the network.
I I IS
+
ε
Collect distributions of infected visitors/conferenceparticipants at the end of each day by varying the seed(inter day variability).Dependence of the epidemic spreading during a single dayon the choice of the seed (intra day variability).
Deterministic SI model 1/2
SI model S + I → 2I, infection probability ε.Set ε = 1: snowball deterministic model (avoidstochasticity).Beyond epidemiology: paradigm for information diffusionand causality on the network.
I I IS
+
ε
Collect distributions of infected visitors/conferenceparticipants at the end of each day by varying the seed(inter day variability).
Dependence of the epidemic spreading during a single dayon the choice of the seed (intra day variability).
Deterministic SI model 1/2
SI model S + I → 2I, infection probability ε.Set ε = 1: snowball deterministic model (avoidstochasticity).Beyond epidemiology: paradigm for information diffusionand causality on the network.
I I IS
+
ε
Collect distributions of infected visitors/conferenceparticipants at the end of each day by varying the seed(inter day variability).Dependence of the epidemic spreading during a single dayon the choice of the seed (intra day variability).
Deterministic SI model 2/2
Processes of and on the networkpartially aggregated network [human contacts]transmission network [information spreading].transmission network ⊆ partially aggregated networknodes outside seed’s CC cannot be reached by infection
Fastest path 6= shortest path.
Deterministic SI model 2/2
Processes of and on the networkpartially aggregated network [human contacts]transmission network [information spreading].transmission network ⊆ partially aggregated networknodes outside seed’s CC cannot be reached by infection
Fastest path 6= shortest path.
Deterministic SI model 2/2
Processes of and on the networkpartially aggregated network [human contacts]transmission network [information spreading].transmission network ⊆ partially aggregated networknodes outside seed’s CC cannot be reached by infection
Fastest path 6= shortest path.
Inter day variability
Nsus for a given seed ≡ number of individuals in the seed’sCC.In a static network framework, P(Ninf/Nsus) = δ( Ninf
Nsus− 1).
Information propagates differently at HT09 and SG.
HT09
Ninf/Nsus
P(N
inf/N
sus)
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
SG
Ninf/Nsus
P(N
inf/N
sus)
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.2 0.4 0.6 0.8 1.0
Intra day variability
Impact of social events (e.g. coffee breaks).Highlight role played by each seed (hard to achieve in astatic network framework).
HT09: June, 30th
Time
Inciden
cecu
rve
0
20
40
60
80
100
08:00 10:00 12:00 14:00 16:00 18:00 20:00
8:00 to 9:009:00 to 10:0010:00 to 11:0011:00 to 12:0012:00 to 13:0013:00 to 14:0014:00 to 15:0015:00 to 16:0016:00 to 17:00
SG: July, 14th
Time
Inciden
cecu
rve
0
50
100
150
200
250
300
12:00 14:00 16:00 18:00 20:00
12:00 to 13:0013:00 to 14:0014:00 to 15:0015:00 to 16:0016:00 to 17:0017:00 to 18:0018:00 to 19:0019:00 to 20:00
Kinetics of information spreading 1/2
Examples from collected data at HT09Network diameters going back and forth in time.
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Kinetics of information spreading 2/2
Distribution of shortest vs fastest path length.SG: May, 19th
nd
P(n
d)
0.0
0.1
0.2
0.3
1 2 3 4 5 6 7 8 9 10
Transmission networkAggregated network
SG: May, 20th
nd
P(n
d)
0.0
0.1
0.2
0.3
0.4
1 3 5 7 9 11 13
Transmission networkAggregated network
SG: July, 14th
nd
P(n
d)
0.0
0.1
0.2
0.3
1 3 5 7 9 11 13 15 17 19
Transmission networkAggregated network
HT09: June, 30th
nd
P(n
d)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 3 5 7 9 11
Transmission networkAggregated network
Conclusions
Network aggregation over different time periods(seasonality, trends, etc..).Network static properties (P(k), diameter, assortativity,etc..).Network dynamic properties: spread of epidemics anddiffusion processes on a longitudinal network hencedynamics of the network and dynamics on the network.What’s in a crowd? Analysis of face-to-face behavioralnetworks, L.Isella et al.http://arxiv.org/abs/1006.1260.
Conclusions
Network aggregation over different time periods(seasonality, trends, etc..).Network static properties (P(k), diameter, assortativity,etc..).Network dynamic properties: spread of epidemics anddiffusion processes on a longitudinal network hencedynamics of the network and dynamics on the network.What’s in a crowd? Analysis of face-to-face behavioralnetworks, L.Isella et al.http://arxiv.org/abs/1006.1260.
Conclusions
Network aggregation over different time periods(seasonality, trends, etc..).Network static properties (P(k), diameter, assortativity,etc..).Network dynamic properties: spread of epidemics anddiffusion processes on a longitudinal network hencedynamics of the network and dynamics on the network.What’s in a crowd? Analysis of face-to-face behavioralnetworks, L.Isella et al.http://arxiv.org/abs/1006.1260.
Conclusions
Network aggregation over different time periods(seasonality, trends, etc..).Network static properties (P(k), diameter, assortativity,etc..).Network dynamic properties: spread of epidemics anddiffusion processes on a longitudinal network hencedynamics of the network and dynamics on the network.What’s in a crowd? Analysis of face-to-face behavioralnetworks, L.Isella et al.http://arxiv.org/abs/1006.1260.
Conclusions
Network aggregation over different time periods(seasonality, trends, etc..).Network static properties (P(k), diameter, assortativity,etc..).Network dynamic properties: spread of epidemics anddiffusion processes on a longitudinal network hencedynamics of the network and dynamics on the network.What’s in a crowd? Analysis of face-to-face behavioralnetworks, L.Isella et al.http://arxiv.org/abs/1006.1260.
Conclusions
Network aggregation over different time periods(seasonality, trends, etc..).Network static properties (P(k), diameter, assortativity,etc..).Network dynamic properties: spread of epidemics anddiffusion processes on a longitudinal network hencedynamics of the network and dynamics on the network.What’s in a crowd? Analysis of face-to-face behavioralnetworks, L.Isella et al.http://arxiv.org/abs/1006.1260.
Acknowlegements
Michael John Gorman, director of the Science Gallery atTrinity College, Dublinhttp://sciencegallery.com/content/science-gallery-2009-infectious
Organizers of Hypertext 2009 conferencehttp://www.ht2009.org/
SocioPatterns project and partnershttp://www.sociopatterns.org
DynaNets projecthttp://www.dynanets.org/
Thank you for your attention!
Acknowlegements
Michael John Gorman, director of the Science Gallery atTrinity College, Dublinhttp://sciencegallery.com/content/science-gallery-2009-infectious
Organizers of Hypertext 2009 conferencehttp://www.ht2009.org/
SocioPatterns project and partnershttp://www.sociopatterns.org
DynaNets projecthttp://www.dynanets.org/
Thank you for your attention!
Acknowlegements
Michael John Gorman, director of the Science Gallery atTrinity College, Dublinhttp://sciencegallery.com/content/science-gallery-2009-infectious
Organizers of Hypertext 2009 conferencehttp://www.ht2009.org/
SocioPatterns project and partnershttp://www.sociopatterns.org
DynaNets projecthttp://www.dynanets.org/
Thank you for your attention!
Acknowlegements
Michael John Gorman, director of the Science Gallery atTrinity College, Dublinhttp://sciencegallery.com/content/science-gallery-2009-infectious
Organizers of Hypertext 2009 conferencehttp://www.ht2009.org/
SocioPatterns project and partnershttp://www.sociopatterns.org
DynaNets projecthttp://www.dynanets.org/
Thank you for your attention!
Acknowlegements
Michael John Gorman, director of the Science Gallery atTrinity College, Dublinhttp://sciencegallery.com/content/science-gallery-2009-infectious
Organizers of Hypertext 2009 conferencehttp://www.ht2009.org/
SocioPatterns project and partnershttp://www.sociopatterns.org
DynaNets projecthttp://www.dynanets.org/
Thank you for your attention!
Acknowlegements
Michael John Gorman, director of the Science Gallery atTrinity College, Dublinhttp://sciencegallery.com/content/science-gallery-2009-infectious
Organizers of Hypertext 2009 conferencehttp://www.ht2009.org/
SocioPatterns project and partnershttp://www.sociopatterns.org
DynaNets projecthttp://www.dynanets.org/
Thank you for your attention!
