ICCS2010

Dynamical networks of person to personinteractions from RFID sensor 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.

ICCS, Amsterdam, Holland, 2010

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).

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).

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).

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).

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).

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).

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).

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

Deterministic SI model 1/3

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 1/3

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 1/3

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 2/3

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

Deterministic SI model 3/3

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

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

Conclusions

A posteriori validation of the infrastructure bypost-processing the collected data.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.

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!

Information diffusion on the network

Aggregated network (since introduction of the seed) andtransmission network.Branching nature of information diffusion.Network diameter going back and forth in time.Fastest path 6= shortest path.

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