Bringing together what belongs together
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Bringing together what belongs together Bringing together what belongs together Presentation Transcript

  • 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
  • The Idea:
    Spot unwanted fragmentation
    recommend a flashmeeting
    The Data: ECTEL, flashmeeting
    The Method(s)
    First results
    Evaluation
    Outline
  • Beforewebegin…
  • (96dpi)
    Knowledgecouldbethedeltaatthereceiver(apaper,ahuman,alibrary).
    Informationcouldbethequalityofacertainsignal.
    Informationcouldbealogicalabstractor.
    Sciencecouldbeaboutsystematicallygivingbirthtoinformationinordertocreateknowledge
    Science,Information,Knowledge
  • Researchers(people,artefacts,andtools)invariouslocationswithheterogeneousaffiliations,purposes,styles,objectives,etc.
    Networkeffectsmakethenetworkexponentiallymorevaluablewithgrowingsize
    Todevelopasharedunderstandingispartoftheresearchworkbecauselanguageunderspecifiesmeaning:future‘cloud’researchwillbuildonit
    Andatthesametime:linguisticrelativity(Sapir-Whorfhypothesis):languageculturerestrictsourthinking
    Scienceismadeinnetworks
  • TheIdea
  • 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!
  • TheData
  • ECTEL Meta-Data
  • Meeting data is open (xml!)
    Complex database behind
    we use rather small subset
    Flashmeeting
  • TheMethod(s)
  • Closenesshowclosetoallothers
    DegreeCentralitynumberof(in/out)connectionstoothers
    Betweennesshowoftenintermediary
    Componentse.g.kmeanscluster(k=3)
    (Social)NetworkAnalysis(S/NA)
  • MeaningfulInteractionAnalysis(MIA)
    Making sense of latent-semantic networks.
  • 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)
  • Pattern: Communitiesarefarfromhomogeneous.Sub-groupscanemerge,particularlyinbigcommunities,whichareconnectedbyasmallset(twoorthree)ofmembersactingasbridgebuildersbetweenotherwisedisconnectedcomponentsintheinteractiongraph.
    Intervetion: Alertsaboutsuchstructuraldysfunctionsincludingtheprovisionofsolutionssuchasjointvirtualmeetingscanhelptomendthemandimproveeffectivecollaborationinsidetheglobalcommunity.
    The Defragmenter (2)
  • FirstResults
  • 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
  • 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
  • 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
  • Evaluation
  • 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
  • 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
  • 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
    Our Preliminary Plan
  • Conclusion
  • 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
  • Beware, the end is near.