Bringing together what belongs

togetherFridolin Wild1), Xavier Ochoa2), Nina Heinze3), Raquel Crespo4),

Kevin Quick1)

1) The Open University, UK, 2) ESPOL, Ecuador3) KMRC, Germany, 4) UC3M, Spain

Outline

• The Idea: – Spot unwanted

fragmentation– recommend a

flashmeeting• The Data: ECTEL,

flashmeeting• The Method(s)• First results• Evaluation

Before we begin…

Information could be the quality of a certain signal.Science could be about systematically giving birth to information in order to create knowledge

Information could be a logical abstractor.Knowledge could be the delta at the receiver (a paper, a human, a library).

Science, Information, Knowledge

(96dpi)

Science is made in networks

Researchers (people, artefacts, and tools) in various locations with heterogeneous affiliations, purposes, styles, objectives, etc.

Network effects make the network exponentially more valuable with growing size

To develop a shared understanding is part of the research work because language underspecifies meaning: future ‘cloud’ research will build on it

And at the same time: linguistic relativity (Sapir-Whorf hypothesis): language culture restricts our thinking

The Idea

Spot unwanted fragmentation!

The goal of developing a recommender for flashmeeting is to use meta data ‐ to support researchers by pointing out other projects, researchers, or related topics they may not be aware of yet and that are closely related to their field of interest.

The Data

ECTEL

Meta-

Data

Flashmeeting

Meeting data is open (xml!)Complex database behind

we use rather small subset

The Method(s)

Degree Centralitynumber of (in/out) connections to others

Closenesshow close to all others

Betweennesshow often intermediary

Componentse.g. kmeans cluster (k=3)

(Social) Network Analysis (S/NA)

Meaningful Interaction Analysis (MIA)Making sense of latent-semantic networks.

The Defragmenter (1)

Pattern: from the co authorship network and ‐the co citations therein, a recommender ‐can identify when authors are working on the same topic (=keywords) but with different co authors and different literature. ‐

Intervention: propose to hold a ’get to know each other' Flashmeeting that may initiate desired defragmentation.

The Defragmenter (2)

Pattern: Communities are far from homogeneous. Sub-groups can emerge, particularly in big communities, which are connected by a small set (two or three) of members acting as bridge builders between otherwise disconnected components in the interaction graph.

Intervetion: Alerts about such structural dysfunctions including the provision of solutions such as joint virtual meetings can help to mend them and improve effective collaboration inside the global community.

First Results

Defrag meeting recommender

Spot unwanted fragmentatione.g. two authors work on the

same topic, but with different collaborator groups and with different literature

Intervention Instrument: automatically recommend

to hold a flashmeeting

Creating cohesion:defragment two groups

Communities are often not very dense, i.e. not resilient

With key persons withdrawing, the network can fragment

Recommend to build additional links, cutting out the middleman

More! FM Recommenders

• Group proposal recommendation: existing cliques can be discovered from graph components, recommending their members to form a group for supporting the management of joint meetings.

• Group closing recommendation: lack of activity in a group may indicate that it no longer exists as such. Confirming group disappearance would be necessary for keeping the server tidy and an accurate map of existing active communities.

• Group access recommender: when raising awareness about existing groups for a given individual, the participation of his/her contacts in a certain group is a strong indicator about the interest of the group for such a person. Recommendations for joining a given group based on contacts’ membership can help to avoid missing information.

• Meeting invitation recommender: awareness of community specific events can also be improved. Based on the known participants in the event as well as their contact relations, recommendations can be made for potential attendants.

Evaluation

Evolution-based evaluation

• The social network structure evolves in time• Compare recommendations based on

historical network data with links actually established (for a certain instant)

PROS: evaluation based on objective data CONS: lack of awareness (instead of non-relevance) can explain recommended connections not appearing in the real network

User-based evaluation

• Ask the user about the quality of the recommendations explicitly

• Questionnaire– Quantitative data (evaluation

metric)– Qualitative data (justification)

• Sample– Depends on actual recommendations

User-based evaluation

• PROS: – More accurate rewarding of

recommendations rising awareness – Deeper insight thanks to qualitative

information • CONS: – Missed links to recommend– Subjective information

(may be affected by other factors)– Data gathering– Statistical significance (sample size)

Structure-based evaluation

• Delete a sample of direct links and check if the system is able to rebuild the network, suggesting them as recommended collaborations.

• PROS: – Based on objective data

• CONS: – Deletions affect the

network structure

Our Preliminary Plan

• Use user survey:– ask for individual relevance ratings

of each recommendation (likert scale)– Stray in random distractors and use them as

a control group to test significance

Conclusion

Defragment today: http://fm.ea-tel.eu

Science requires networks.To understand, networks

need to communicate.With recommender systems unwanted

fragmentation can be spotted.

And interventions be scheduled.

Beware, the end is near.