Les Houches

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!