The document discusses a study that deployed RFID sensors to collect data on human interactions in different social environments. Specifically, it focuses on collecting data at the Science Gallery in Dublin over 3 months and at the HT09 conference in Turin over 3 days. The goal is to analyze dynamics of person-to-person networks and apply the findings to computational epidemiology and other fields. The RFID sensors allow representation of physical proximity as a network with high spatial and temporal resolution.

1. Dynamical networks of person to person
interactions from RFID sensor networks
Lorenzo Isella1 , Alain Barrat1,2 , Juliette Stehlé2 ,
Jean-François Pinton3 , Wouter Van den Broeck1 and
Ciro Cattuto1
1 Complex Networks and Systems Group, Institute for Scientific Interchange (ISI)
Foundation, Turin, Italy.
2 Centre de Physique Théorique, CNRS UMR 6207, Marseille, France.
3 Laboratoire de Physique de l’ENS Lyon, CNRS UMR 5672, Lyon, France.
ICCS, Amsterdam, Holland, 2010

2. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

3. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

4. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

5. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

6. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

7. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

8. Goals and Case Studies
Deployment of wearable RFID devices to collect data
about 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).

9. Overview of the Infrastructure
Tags exchange packets at various powers and report their
contacts to antennas broadcasting the data to a server.
Low-power packets expose face-to-face interactions at
small distances (∼ 1m).

10. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

11. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

12. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

13. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

14. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

15. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

16. From Physical Proximity to Networks
Natural representation of physical proximity as a network in
addition to
scalability
unobtrusiveness
low cost
high spatial resolution ∼ 1 meter
high temporal resolution ∼ 5 − 20 seconds.

17. Aggregated Networks
Aggregate all the contacts along 24 hours.
HT09: June, 30th SG: July, 14th
q q
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q
q
q q
q
q
q q
q
q q q
q
SG: May, 19th SG: May, 20th
q q
q
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q
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18. Human Dynamics and Network Topology 1/2
Entanglement of human behavior and network topology.
0.008
P (visit duration)
0.006
0.004
0.002
0.000
101 102
Visit duration (min)
12:00 to 13:00
13:00 to 14:00
14:00 to 15:00
15:00 to 16:00
16:00 to 17:00
17:00 to 18:00
18:00 to 19:00
19:00 to 20:00

19. Human Dynamics and Network Topology 2/2
Short-tailed P(k ) and broad P(wij ) and P(∆tij ).
SG HT09
10-1 10-1
10-2
10-2
P (k)
P (k)
-3
10
10-3
10-4
10-5 10-4
0 10 20 30 40 50 60 70 0 20 40 60 80
k k
100 100
-1
10 SG
10-1 SG
HT09 HT09
10-2 -2
10
P (∆tij )
P (wij )
10-3
10-3
10-4
10-5 10-4
-6
10 10-5
101 102 103 104 101 102 103 104
∆tij (sec) wij (sec)

20. Random Networks and Smallworldness
Network topology↔ information spreading.
HT09: June, 30th (rewired) SG: July, 14th (rewired)
q
q
q
q
q
q
q
q
q
q
q
HT09: June, 30th SG: July, 14th
1.0 1.0
0.8
0.8
M(l)/M(∞)
M(l)/M(∞)
0.6
0.6
0.4
0.4
Aggregated network 0.2 Aggregated network
Rewired networks Rewired networks
0.2 0.0
1 2 3 4 1 2 3 4 5 6 7 8 9 10
l l

21. Deterministic SI model 1/3
SI model S + I → 2I, infection probability .
Set = 1: snowball deterministic model (avoid
stochasticity).
Beyond epidemiology: paradigm for information diffusion
and causality on the network.
I S I I
+

22. Deterministic SI model 1/3
SI model S + I → 2I, infection probability .
Set = 1: snowball deterministic model (avoid
stochasticity).
Beyond epidemiology: paradigm for information diffusion
and causality on the network.
I S I I
+
Collect distributions of infected visitors/conference
participants at the end of each day by varying the seed
(inter day variability).

23. Deterministic SI model 1/3
SI model S + I → 2I, infection probability .
Set = 1: snowball deterministic model (avoid
stochasticity).
Beyond epidemiology: paradigm for information diffusion
and causality on the network.
I S I I
+
Collect distributions of infected visitors/conference
participants at the end of each day by varying the seed
(inter day variability).
Dependence of the epidemic spreading during a single day
on the choice of the seed (intra day variability).

24. Deterministic SI model 2/3
Nsus for a given seed ≡ number of individuals in the seed’s
CC.
Ninf
In a static network framework, P(Ninf /Nsus ) = δ( Nsus − 1).
Information propagates differently at HT09 and SG.
HT09 SG
1.0 1.0
0.8 0.8
P (Ninf /Nsus )
P (Ninf /Nsus )
0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0
Ninf /Nsus Ninf /Nsus

25. Deterministic SI model 3/3
Impact of social events (e.g. coffee breaks).
Highlight role played by each seed (hard to achieve in a
static network framework).
HT09: June, 30th SG: July, 14th
300
100 8:00 to 9:00 12:00 to 13:00
9:00 to 10:00 13:00 to 14:00
10:00 to 11:00 250 14:00 to 15:00
80 11:00 to 12:00 15:00 to 16:00
Incidence curve
Incidence curve
12:00 to 13:00 16:00 to 17:00
13:00 to 14:00 200 17:00 to 18:00
14:00 to 15:00 18:00 to 19:00
60 15:00 to 16:00 19:00 to 20:00
16:00 to 17:00 150
40
100
20 50
0 0
08:00 10:00 12:00 14:00 16:00 18:00 20:00 12:00 14:00 16:00 18:00 20:00
Time Time

26. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

27. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

28. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

29. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

30. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

31. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

32. Conclusions
A posteriori validation of the infrastructure by
post-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 and
diffusion processes on a longitudinal network hence
dynamics of the network and dynamics on the network.

33. Acknowlegements
Michael John Gorman, director of the Science Gallery at
Trinity College, Dublin
http://sciencegallery.com/content/
science-gallery-2009-infectious
Organizers of Hypertext 2009 conference
http://www.ht2009.org/
SocioPatterns project and partners
http://www.sociopatterns.org
DynaNets project
http://www.dynanets.org/
Thank you for your attention!

34. Acknowlegements
Michael John Gorman, director of the Science Gallery at
Trinity College, Dublin
http://sciencegallery.com/content/
science-gallery-2009-infectious
Organizers of Hypertext 2009 conference
http://www.ht2009.org/
SocioPatterns project and partners
http://www.sociopatterns.org
DynaNets project
http://www.dynanets.org/
Thank you for your attention!

35. Acknowlegements
Michael John Gorman, director of the Science Gallery at
Trinity College, Dublin
http://sciencegallery.com/content/
science-gallery-2009-infectious
Organizers of Hypertext 2009 conference
http://www.ht2009.org/
SocioPatterns project and partners
http://www.sociopatterns.org
DynaNets project
http://www.dynanets.org/
Thank you for your attention!

36. Acknowlegements
Michael John Gorman, director of the Science Gallery at
Trinity College, Dublin
http://sciencegallery.com/content/
science-gallery-2009-infectious
Organizers of Hypertext 2009 conference
http://www.ht2009.org/
SocioPatterns project and partners
http://www.sociopatterns.org
DynaNets project
http://www.dynanets.org/
Thank you for your attention!

37. Acknowlegements
Michael John Gorman, director of the Science Gallery at
Trinity College, Dublin
http://sciencegallery.com/content/
science-gallery-2009-infectious
Organizers of Hypertext 2009 conference
http://www.ht2009.org/
SocioPatterns project and partners
http://www.sociopatterns.org
DynaNets project
http://www.dynanets.org/
Thank you for your attention!

38. Acknowlegements
Michael John Gorman, director of the Science Gallery at
Trinity College, Dublin
http://sciencegallery.com/content/
science-gallery-2009-infectious
Organizers of Hypertext 2009 conference
http://www.ht2009.org/
SocioPatterns project and partners
http://www.sociopatterns.org
DynaNets project
http://www.dynanets.org/
Thank you for your attention!

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