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Contributed talk given at ICCS2010 (http://www.iccs-meeting.org/)

Contributed talk given at ICCS2010 (http://www.iccs-meeting.org/)

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ICCS2010 ICCS2010 Presentation Transcript

  • 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
  • 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).
  • 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).
  • 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).
  • 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).
  • 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).
  • 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).
  • 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).
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Aggregated Networks Aggregate all the contacts along 24 hours. HT09: June, 30th SG: July, 14th q q q q q q q q q q q q q q q q SG: May, 19th SG: May, 20th q q q q q q q q q q q q q q q q q q q q q q q q
  • 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
  • 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)
  • 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
  • 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 +
  • 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).
  • 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).
  • 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
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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!
  • 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!
  • 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!
  • 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!
  • 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!
  • 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!
  • 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. q qqq q qq q q q q q q q qqq qq q qqqq q q qq q q q q q q q q qqqqqqqqqqqqq qqq q q q q q qqq qq q q q q q q q q qq qq qq q q qqqqq qqqqqqqqqqqqqqqq qq qqqq q q q q q q q q q qqqqq qq q q q q q q qqq qqqq qq q q q qqqqq q q q q q q q q q qqq qq q q