Co-presence Communities

Using pervasive computing to support weak social networks

Jamie Lawrence

Terry Payne & David De Roure

DMC 2006

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

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.

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.

Co-presence Detection

• Must correspond to human sensory limits

• Ego-centric– Bluetooth– IrDA badges

• Omniscient– GPS tracking

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, …

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

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}>

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

Discretisation

• Transform the co-presence events into discrete time slots

• Useful if the data comes from multiple sources

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

0

1

-1

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

Worked Example

Interval 1 Interval 2 Interval 3 Interval 4

Day 1 B B,C D

Day 2 C B,C D

Day 3 D B,C D

Day 4 E B,C D

Day 5 F B,C D

Day 1: Periods

Day 1: Clusters

Day 2: Periods

Day 2: Clusters

Day 3: Periods

Day 3: Clusters

Day 4: Periods

Day 4: Clusters

Day 5: Periods

Day 5: Clusters

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

Questions?jamie.lawrence@soton.ac.uk

http://eprints.ecs.soton.ac.uk/12684/