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Cluestr: Mobile Social Networking for Enhanced Group Communication. Reto Grob (Swisscom) Michael Kuhn (ETH Zurich) Roger Wattenhofer (ETH Zurich) Martin Wirz (ETH Zurich) GROUP 2009 Sanibel Island, FL, USA. Biggest online social network?. Facebook (200M). Orkut (67M). MySpace (250M).

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Cluestr mobile social networking for enhanced group communication l.jpg

Cluestr: Mobile Social Networking for Enhanced Group Communication

Reto Grob (Swisscom)

Michael Kuhn (ETH Zurich)

Roger Wattenhofer (ETH Zurich)

Martin Wirz (ETH Zurich)

GROUP 2009

Sanibel Island, FL, USA


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Biggest online social network? Communication

Michael Kuhn, ETH Zurich @ GROUP 2009


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Facebook Communication

(200M)

Orkut

(67M)

MySpace

(250M)

Classmates

(50M)

LinkedIn

(35M)

Windows Live

Spaces (120M)

E-Mail

(1.6B Internet users)

(March 2009)

Mobile Phone Contact Book

(4B mobile subscribers)

(March 2009)

Michael Kuhn, ETH Zurich @ GROUP 2009


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borders between offline and online interaction are diminishing

Michael Kuhn, ETH Zurich @ GROUP 2009


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social interaction gets mobile diminishing

Michael Kuhn, ETH Zurich @ GROUP 2009


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online communication gets mobile diminishing

virtual meets real-world communication

mobile group interaction

Michael Kuhn, ETH Zurich @ GROUP 2009


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„There‘s no diminishing

training tonight!“

„What movie are we

going to watch?“

„Be home

at 8pm!“

Our Survey

(342 participants from Europe)

little support in current devices

hardly anybody is willing to manually maintain groups

Michael Kuhn, ETH Zurich @ GROUP 2009


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How to bridge this gap? diminishing

Our approach:

mechansim for group initialization on mobile devices

Michael Kuhn, ETH Zurich @ GROUP 2009


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updated group diminishing

recommended contacts

group

(i.e. „invited“ contacts)

new recommendations

Michael Kuhn, ETH Zurich @ GROUP 2009


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How to know which contacts to recommend? diminishing

manual grouping

analysis of communication patterns

analysis of social network

semantic analysis

Michael Kuhn, ETH Zurich @ GROUP 2009


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Architecture diminishing

Michael Kuhn, ETH Zurich @ GROUP 2009


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social network => recommendation? diminishing

recommend best connected contacts

Either:device needs to know inter-friend-connections

=> privacy

Or:server needed for each recommendation step

=> server load

=> tunnel/mountains

=> traffic/costs

clustering

Michael Kuhn, ETH Zurich @ GROUP 2009


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clusters approximate communities! diminishing

Michael Kuhn, ETH Zurich @ GROUP 2009


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only once for entire recommendation process

if no connection available, old data can be used

Michael Kuhn, ETH Zurich @ GROUP 2009


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4 diminishing

6

currently invited group

Michael Kuhn, ETH Zurich @ GROUP 2009


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Hierarchical, divisive algorithm to cluster undirected, unweighted networks

Based on algorithm presented by Girwan an Newman in 2002

Extended to allow overlapping clusters

CONGA

S. Gregory. An algorithm to find overlapping community structure in networks.

In PKDD, 2007

Michael Kuhn, ETH Zurich @ GROUP 2009


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cluestr unweighted networks

Michael Kuhn, ETH Zurich @ GROUP 2009


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Clustering accurracy unweighted networks

How well do clusters represent communities?

Effect of sparsity

How well do algorithms perform in bootstrapping phase?

Performance of group initialization

How much time can be saved during group initialization?

Evaluation

Michael Kuhn, ETH Zurich @ GROUP 2009


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Friend-of-friend information for mobile phone contacts not available

Facebook data

4 subjects (2 male, 2 female)

assigned contacts to communities

Ground Truth

Michael Kuhn, ETH Zurich @ GROUP 2009


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identified by algorithm available

identified by subjects (ground truth)

F-measure:

Michael Kuhn, ETH Zurich @ GROUP 2009


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Clustering Accuracy available

  • How well do clusters represent communities?

  • Number of clusters well matches number of communities

Michael Kuhn, ETH Zurich @ GROUP 2009


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Effects of Sparsity available

How well does clustering work under such conditions?

  • Bootstrapping

    • Only few participants

    • Missing friendship links

  • Randomly removed links (10%-90%)

  • Randomly removed nodes (10%-90%)

cluster sizes shrink only slowely

precision stays,

recall moderately decays

precision and recall only slightly decay

non-existing nodes cannot be recommended

Michael Kuhn, ETH Zurich @ GROUP 2009


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Time Savings available

Community related: Considerable time savings

Random:

only slightly slower

Sending message to contacs of a community

Sending message to some contacs of a community

Sending message to random contacts

Michael Kuhn, ETH Zurich @ GROUP 2009


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We have shown that: available

Social network contains community information

This information can be extracted by clustering algorithms

The clusters can be used for contact recommendation

Such recommendations save a significant amount of time

Our work bridges gap identified by our survey:

Group interaction is important, but badly supported by current devices

Conclusion

Michael Kuhn, ETH Zurich @ GROUP 2009


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Questions? available

Michael Kuhn, ETH Zurich @ GROUP 2009


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