Co-presence Communities

1. Co-presence Communities • Jamie Lawrence • Terry Payne & David De Roure • DMC 2006 Using pervasive computing to support weak social networks

2. Introduction http://eprints.ecs.soton.ac.uk/12684/ Focus on relating this work to the DMC workshop (and WETICE in general) • Weak Social Networks • Co-presence • Co-presence Communities • Application • Discovery Algorithm • Worked Example

3. Weak Social Networks • Weak & Informal Networks • Familiar Stranger • Communities of Practice • Shrinking circle of “best friends” • Weak relationships are important • Familiar Strangers provide social support in times of crisis • CoP are vital sources of information and expertise in an enterprise • Ironically, weak relationships are often • based on physical interaction • least served by technological solutions.

4. Co-presence • “sense that they are close enough to be perceived in whatever they are doing, including their experiencing of others, and close enough to be perceived in this sensing of being perceived” – Goffman • “corporeal copresence” – Zhao’s taxonomy • Natural state of co-presence: all parties are physically proximate and present at the same site.

5. Co-presence Detection • Must correspond to human sensory limits • Ego-centric • Bluetooth • IrDA badges • Omniscient • GPS tracking

6. Co-presence Communities A group of people that you are usually around during a particular time period • People • Time • Context must be defined by the user • regular meeting, sports club, lunch, coffee break, Friday evening pints, …

7. Applications • Ambient Information Dissemination Environment (AIDE) • Use Co-presence Communities to control the flow of information • For example, distributing a URL to the “afternoon coffee crew” • Context-aware computing • Co-presence Communities can add context to other information sources, e.g. diaries • Building a social networking service from real-world interaction data

8. Mining Algorithm Attributes • Incremental • Probabilistic • High-dimensional data • Error smoothing (missing values) • Transform from… • <start, end, device, device> • <time, device, device> • To… • <~start, ~end, ~{devices}>

9. Mining Algorithm Overview • Discretisation • Produces groups of co-present devices at each time interval • Feature Extraction • Finds periods of continuously similar co-presence • Clustering • Cluster the co-presence periods across all historical data • The clusters provide the Co-presence Community definitions

10. Discretisation • Transform the co-presence events into discrete time slots • Useful if the data comes from multiple sources

11. Feature Extraction • Detects changes in the co-presence membership • Use a Laplacian of Gaussian (LoG) edge detection routine averaged across devices • Period boundaries occur at the zero-crossings 1 0 -1

12. Clustering • Clusters periods of co-presence together • Uses a implementation of COBWEB • Modified to accept Nominal Set attributes • The resulting clusters define the co-presence communities • Can be weighted to find temporal or membership-stable communities

13. Worked Example

14. Day 1: Periods

15. Day 1: Clusters

16. Day 2: Periods

17. Day 2: Clusters

18. Day 3: Periods

19. Day 3: Clusters

20. Day 4: Periods

21. Day 4: Clusters

22. Day 5: Periods

23. Day 5: Clusters

24. Conclusions • Introduced the idea of Co-presence Communities • Discussed how they might capture weak social networks • Presented a method of discovering these communities • Demonstrated a simple example

25. Questions? jamie.lawrence@soton.ac.uk http://eprints.ecs.soton.ac.uk/12684/