1. Bringing together what belongs together Fridolin Wild1), Xavier Ochoa2), Nina Heinze3), Raquel Crespo4), Kevin Quick1)1) The Open University, UK, 2) ESPOL, Ecuador3) KMRC, Germany, 4) UC3M, Spain

2. The Idea: Spot unwanted fragmentation recommend a flashmeeting The Data: ECTEL, flashmeeting The Method(s) First results Evaluation Outline

3. Beforewebegin…

4. (96dpi) Knowledgecouldbethedeltaatthereceiver(apaper,ahuman,alibrary). Informationcouldbethequalityofacertainsignal. Informationcouldbealogicalabstractor. Sciencecouldbeaboutsystematicallygivingbirthtoinformationinordertocreateknowledge Science,Information,Knowledge

5. Researchers(people,artefacts,andtools)invariouslocationswithheterogeneousaffiliations,purposes,styles,objectives,etc. Networkeffectsmakethenetworkexponentiallymorevaluablewithgrowingsize Todevelopasharedunderstandingispartoftheresearchworkbecauselanguageunderspecifiesmeaning:future‘cloud’researchwillbuildonit Andatthesametime:linguisticrelativity(Sapir-Whorfhypothesis):languageculturerestrictsourthinking Scienceismadeinnetworks

6. TheIdea

7. 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. Spot unwanted fragmentation!

8. TheData

9. ECTEL Meta-Data

10. Meeting data is open (xml!) Complex database behind we use rather small subset Flashmeeting

11. TheMethod(s)

12. Closenesshowclosetoallothers DegreeCentralitynumberof(in/out)connectionstoothers Betweennesshowoftenintermediary Componentse.g.kmeanscluster(k=3) (Social)NetworkAnalysis(S/NA)

13. MeaningfulInteractionAnalysis(MIA) Making sense of latent-semantic networks.

14. 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 (1)

15. Pattern: Communitiesarefarfromhomogeneous.Sub-groupscanemerge,particularlyinbigcommunities,whichareconnectedbyasmallset(twoorthree)ofmembersactingasbridgebuildersbetweenotherwisedisconnectedcomponentsintheinteractiongraph. Intervetion: Alertsaboutsuchstructuraldysfunctionsincludingtheprovisionofsolutionssuchasjointvirtualmeetingscanhelptomendthemandimproveeffectivecollaborationinsidetheglobalcommunity. The Defragmenter (2)

16. FirstResults

17. Spot unwanted fragmentation e.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 Defrag meeting recommender

18. 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 Creating cohesion:defragment two groups

19. 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. More! FM Recommenders

20. Evaluation

21. The social network structure evolves in time Compare recommendations based on historical network data with links actually established (for a certain instant) CONS: lack of awareness (insteadof non-relevance) can explain recommended connections not appearing in the real network PROS: evaluation based on objective data Evolution-based evaluation

22. 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

23. 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)

25. Based on objective data

26. CONS:

28. Conclusion

29. Science requires networks. To understand, networks need to communicate. With recommender systems unwanted fragmentation can be spotted. And interventions be scheduled. Defragment today: http://fm.ea-tel.eu

30. Beware, the end is near.