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I'll preface this by saying I have no knowledge in this area so apologize in advance if my question is bonkers!

Basically, I was hoping someone could point me in the right direction where to start with the below and what approach one should take.

Assuming data that reflect the movement of people through locations where each person and location are uniquely identified, I can use social modelling to find people that have been at the same location. However, there is also a date/time factor in this where two people may have been at the same location but at different times and so have never actually met.

As an example, say:

  • Person 1 went to location A on 12/04/2016 12:00 and then left the location on 13/04/2016 18:00.
  • Person 2 went to location A on 12/04/2016 10:00 and left on 14/04/2016 10:00.
  • Person 3 went to location A on 09/04/2016 11:00 and left on 09/04/2016 18:00.

In that case, persons 1 and 2 are connected due to having been at location A at the same time. Person 3 is connected to neither as although they went to location A, they left prior to the arrival of either person 1 or person 2.

Any help and suggestions with where to start or if this is even feasible would be much appreciated.

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  • $\begingroup$ Not very complicated. Just enter the dates and times in Excel, and If A then if comparing people the latest arrival time occurs before the soonest departure time than they met. If latest arrival occurs after soonest departure, they didn't meet. $\endgroup$ – Carl Sep 22 '16 at 22:56
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There is a wide literature out there on longitudinal social network analysis (SNA). For instance, ETH-Zurich has a group dedicated to SNA and sponsors an annual workshop in longitudinal SNA (see here ... http://www.social-networks.ethz.ch/ and here ... http://www.social-networks.ethz.ch/education/siena-winterschool-on-longitudinal-social-network-analysis.html as well as here ... http://www.stats.ox.ac.uk/~snijders/siena/).

Academic resources include Uddin, et al's article A set of measures to quantify the dynamicity of longitudinal social networks (see here ... http://onlinelibrary.wiley.com/doi/10.1002/cplx.21690/abstractcampaign=wolearlyview). Lylesdorff, et al., have an article Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling (see here ... http://www.leydesdorff.net/socnetw/paper/index.htm). CMU's CASOS group does research in this area (see here ... http://www.casos.cs.cmu.edu/projects/ora/). Another extremely useful resource is the Stanford- and Santa Fe Institute-based academic, Matthew Jackson. He is currently running a Coursera MOOC on SNA that delves into time series aspects of the approach (see here ... https://www.coursera.org/learn/social-economic-networks). This course builds from the fundamentals up. Then there's COSNET (see here ... http://cosnet.bifi.es/).

Good books to look up include Borgatti's Analyzing Social Networks. A reasonable history of SNAs can be found here ... http://www.analytictech.com/networks/history.htm. Easley and Kleinberg's book *Networks, Crowds, and Markets: * is available in a pre-publication version (see here ... http://www.cs.cornell.edu/home/kleinber/networks-book/).

Resources for raw data for SNA include UCiNET datasets (see here ... http://vlado.fmf.uni-lj.si/pub/networks/data/ucinet/ucidata.htm) as well as the Colorado Index of Complex Networks (see here ... https://icon.colorado.edu/#!/).

Let me know if you need more information.

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  • $\begingroup$ Thanks for your reply and that information. I haven't had a chance to look into it yet but hope to do so shortly. Basically, $\endgroup$ – c95mbq Oct 4 '16 at 22:11
  • $\begingroup$ Thanks for your reply. I haven't had a chance to look yet. I hoped to visually illustrate which persons had contact. Currently a report shows people met at location where one drills down into each patient separately to see which people they in turn had contact with. Rather than having to drill down into each and every person separately, it would be neat to be able to just take all of the available transactions and depending on the node that's selected, expand to see edges and nodes this connects to. Again, problem being the time factor as location itself doesn't mean they have had contact $\endgroup$ – c95mbq Oct 4 '16 at 22:19

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