Analysing panel co-attendance in scientific conferences

Presentation at the #Sunbelt2020 virtual session on 𝘽𝙞𝙥𝙖𝙧𝙩𝙞𝙩𝙚 𝙉𝙚𝙩𝙬𝙤𝙧𝙠𝙨 & 𝙋𝙧𝙤𝙟𝙚𝙘𝙩𝙞𝙤𝙣𝙨, #NETCONF research project

Transcript

1. Analysing panel co-attendance
   in scientific conferences
   A new avenue to explore academic sociality
   #Sunbelt2020 virtual sessions on 𝘽𝙞𝙥𝙖𝙧𝙩𝙞𝙩𝙚 𝙉𝙚𝙩𝙬𝙤𝙧𝙠𝙨 &
   𝙋𝙧𝙤𝙟𝙚𝙘𝙩𝙞𝙤𝙣𝙨
   Empirical Insights from Bipartite and Multipartite Networks
   July 15, 2020
   François Briatte · ESPOL, Catholic U. of Lille, France
   Marion Maisonobe · Géocités, CNRS, Paris, France
   

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3. Background (network science + science studies)
   ● Research on scientific collaboration and networks
   mainly relies on co-authorship data
   ● Many other types of interactions contribute to the
   circulation of ideas between scholars and to the
   emergence of scientific groups
   ● Among them, social links derived from participation to
   conferences and panel co-attendance
   ● Interesting to explore, with conference programmes and
   lists of participants often openly accessible on the Web
   

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4. ● Panel co-attendance in two scientific conferences about
   green chemistry (ISGC) and political science (AFSP)
   ● Conference structure (sessions, topics, etc.) and its
   evolution can inform specialty/discipline dynamics
   ● Panel co-attendance used as a proxy for knowledge
   circulation between participants/places
   ● Common methodology applied to both conferences,
   through several conference years
   T (ISGC) = 6 years, T (AFSP) = 10 years (every odd year)
   Goals of the project
   

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5. Project summary
   ● NETCONF project, 2020–21 (funded by URFIST GIS)
   ● Participants: CRIEF (EA, Univ. Poitiers), ESPOL-LAB (Univ.
   Catho. Lille), Géographie-cités (UMR CNRS, Univ. Paris 1,
   Univ. de Paris, EHESS), CESSP (UMR CNRS, Univ. Paris 1),
   ELICO (EA, Univ. Lyon), INCREASE (FR CNRS)
   ● Intended outputs: (1) clean network data, and measures
   of (2) internationalization and (3) geographic span for
   both studied specialties/disciplines
   ● Groundwork for inclusion of additional conferences
   (possibly from different fields)
   

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8. Comparable properties
   ● Both conferences count ~ 700–1,000 participants i, with
   most attending a single panel j
   ● Attending several years is frequent but not the modal
   behaviour (return rate ~ 25% for ISGC, ~ 30% for AFSP)
   ● ISGC (green chemistry, organised in La Rochelle) is much
   more internationalized (50–60%) than AFSP (French or
   Francophone political science, itinerant, max. 20% intl.)
   ● Yet both conferences attract participants from roughly
   the same number of countries (~ 60)
   

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9. Geographic ties based on panel co-attendance
   Francophone countries
   

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10. Each tie captures one
    panel co-attendance in
    the panel/participant
    bipartite graph.
    We used all ties > 1
    (2+ panel co-attendance
    at the same conference)
    to extract the backbone of
    each graph.
    Universal method used,
    but perhaps edge
    distribution justifies
    another approach?
    Edge weight distributions of one-mode projections
    1 tie 2 ties 3 ties 4 ties 5 ties
    ISGC 2015 17080 122 6 4
    ISGC 2017 20444 230 26 4 2
    ISGC 2019 18230 358 24 8 4
    AFSP 2009 14478 230 8
    AFSP 2011 9538 34
    AFSP 2013 15364 100 2
    AFSP 2015 17476 114
    AFSP 2017 9934 52
    AFSP 2019 15180 82
    

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12. Next steps (suggestions welcome)
    ● Weighting scheme of the ( Participant i × Panel j )
    bipartite adjacency matrix: 1/N
    i
    weights?
    ● Additional data on participants (e.g. affiliations,
    publications, dissertation committees) — ongoing work
    ● Characteristics (socio-demographic, geographic) of the
    nodes connected by the network backbone
    ● Temporal analysis with TERGMs? (institution-/city-level
    homophily sustained through conference years; cohorts)
    ● Additional data from similar conferences?
    see experimental takes at epsa2020 and statconf
    

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13. ● ISGC conference data shared by its managing company
    ● AFSP conference data scraped from the AFSP website
    see congres-afsp for the code and (preliminary) data
    ● Graph visualizations performed with igraph (Csárdi),
    ggraph (Pedersen) and graphlayouts (Schoch)
    ● Backbone extraction performed with backbone
    (Domagalski, Neal and Sagan)
    ● References for final report on Zotero
    Sources
    

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14. Thank you for your attention
    Slides at frama.link/netconf-2020-sunbelt
    marion.maisonobe@cnrs.fr · @GeoMaisonobe
    francois.briatte@sciencespo.fr · @phnk
    

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