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What is a Boundary?On Continuity and Density
in the Social Sciences
Tommaso Venturini
Follow the White Rabbitwhy controversy mapping (and digital methods)
will change everything you know about sociology
Tommaso Venturini
The strabismusof social sciences
Photo credit – tarout_sun via Flickr - ©
3 discontinuities
• 1. In data:intensive data / extensive data
• 2. In methods:situating / aggregating
• 3. In theory:micro-interactions / macro-structure
Part IData:
intensive / extensive
The quali/quantitative divide
poor data on large populationextensive data
intensive datarich data on small population
Extensive data Paul Butler, 2010Visualizing Friendships
Intensive data AOL user 711391 search historywww.minimovies.org/documentaires/view/ilovealaska
Extensive andintensive data
Google Fluwww.google.org/flutrends
Extensive andintensive data
Google Fluwww.google.org/flutrends
Extensive andintensive data
Venturini, Tommaso and Bruno Latour, 2010
“The Social Fabric: Digital Traces and Quali-
Quantitative Methods”
in Proceedings of Future En Seine 2009, pp. 87–101
Paris: Editions Future en Seine.
This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.
Chris Andersonhttp://www.wired.com/science/discoveries/magazine/16-07/
pb_theory
The end of theory?
Askitas, N., & Zimmermann, K. 2011 Health and Well-Being in
the Crisis IZA Discussion Paper
Beware: digital datais not your data!
Beware: digital datais not your data!
http://googlesystem.blogspot.fr/
2008/08/google-suggest-enabled-by-default.html
Beware: digital datais not your data!
Part II Methods:
situating /
aggregating
(Collective) lifeis complicated Andreas Gursky 1999
Chicago, Board of Trade II
Situating VS aggregating Armin Linke
Inside / Outside
La fabrique de la loi http://www.lafabriquedelaloi.fr
Extensive andintensive data
Latour, Bruno, Pablo Jensen, Tommaso
Venturini,
Sébastian Grauwin and Dominique Boullier,
2012.
“‘The Whole Is Always Smaller than Its
Parts’:
A Digital Test of Gabriel Tardes’ Monads.”
The British journal of sociology 63(4), pp.
590–615
Part III Theory:
micro-interactions /
macro-structure
The micro/macro boundary
Merian & Jonston 1718 Folio Ants, Clony,
Nest, Insects
Thomas Hobbes, 1651The Leviathan
An ontological andemergent boundary
The collective self is not a simple epiphenomenon of its morphologic base, precisely as the individual self is not a simple efflorescence of the nervous system.
For the collective self to appear, a sui generis synthesis of individual self has to be produced. This synthesis creates a world of feelings, ideas, images that, once come to life, follow their own laws.
Emile Durkheim, 1912Le formes
élémentaires de la vie religieuse
…that may hide othermore relevant boundaries
zgrossbart.github.io/hborecycling/
From boundariesto boundary work
Fences make good neighbors
Gieryn, Thomas F. (1983)Boundary-work
the demarcation of science from non-science
American Sociological Review 48(6): 781–795
Demarcation is as much a practical problem for scientists as an analytical problem for sociologists and philosophers
The lesson of ANT(and of constructivism)
It is not that in collective life there are no boundaries(between micro and macro, science and politics…),
It is that all boundaries are constantly constructed, de-constructed and re-constructed(and this is work is the object of social research)
The lesson of ANT(and of constructivism)
It is not that in collective life there are no boundaries(between micro and macro, science and politics…),
It is that all boundaries are constantly constructed, de-constructed and re-constructed(and this is work is the object of social research)
Venturini, T. (2010).Diving in magma: how to explore controversies with actor-network theory. in Public Understanding of Science, 19(3), 258–273.
Part IV Becoming
sensitive to the
differences in the
density of
association
3 discontinuities
• 1. In data:intensive data / extensive data
• 2. In methods:situating / aggregating
• 3. In theory:micro-interactions / macro-structure
3 discontinuitiesto cross
• 1. In data:intensive data / extensive dataDigital traceability and computation (data geeks)
• 2. In methods:situating / aggregatingDatascape navigation (designers)
• 3. In theory:micro-interactions / macro-structureA non-emergentist theory of action (actor-network theorists)
A network (graph)is not a network (actor-network)
A network (graph)is not a network (actor-network)
Actor-Network Theory Visual Network Analysis
Actors and networks have the same properties (they are the same)
≠Networks are composite while nodes are indivisible and uncombinable
Different mediations (can) have different effects ≠
All edges have the same effect (possibly with different weight)
Different actors (can) have different association potential ≠ All nodes have equal linking
potential
A-N are always seen from one or more specific viewpoints ≠ Networks are usually seen from
above/outside
What counts is change ≠ Networks are statics
A questionof resonance
A diagram of a network, then, does not look like a network but maintain the same qualities of relations – proximities, degrees of separation, and so forth – that a network also requires in order to form.
Resemblance should here be considered a resonating rather than a hierarchy (a form) that arranges signifiers and signified within a sign(p. 24).
Munster, A. (2013).An Aesthesia of Networks
Cambridge Mass.: MIT Press
The fabric of(cooked) rice Roland Barthes (1970)
The Empire of Signs
Cooked rice (whose absolutely special identity is attested by a special name, which is not that of raw rice) can be defined only by a contradiction of substance; it is at once cohesive and detachable; its substantial destination is the fragment, the clump; the volatile conglomerate… it constitutes in the picture a compact whiteness, granular (contrary to that of our bread) and yet friable:
what comes to the table to the table, dense and stuck together, comes undone at a touch of the chopsticks, though without ever scattering, as if division occurred only to produce still another irreducible cohesion (pp. 12-14).
The fabric ofcollective life
Jacob L. Moreno, April 3, 1933The New York Times
Social life is continuous but not homogenousDoing social research is becoming sensitive tothe differences in the density of association
Force-vector algorithms
Force-vectors’ magic trick
Force-vectors’ magic trick
Jacomy, M., Venturini, T., Heymann, S. & Bastian, M.
(2014)
ForceAtlas2, a Continuous Graph Layout Algorithm for
Handy Network Visualization Designed for the Gephi
Software.
PlosONE, 9:6
Network as maps London Underground1920 map
homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
Network as maps London Underground1933 map (Harry Beck)
homepage.ntlworld.com/clivebillson/tube/tube.html - www.fourthway.co.uk/tfl.html
Part VVisual Network
Analysis
Semiologyof graphics Bertin J., Sémiologie graphique,
Paris, Mouton/Gauthier-Villars, 1967
Visual variables
A B
C
Visual network analysis questions
A. Position (force-vector spatialization)
1. Nodes density Where are structural holes (under-populated regions)?Where are clusters an sub-clusters (over-populated regions)?Which are the largest and most cohesive clusters?
2. Relative positionWhich nodes/clusters are globally and locally central?Which nodes/clusters are global and local bridges (between clusters)?
B. Size (ranking by in-degree / out-degree)
3. Nodes connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
Visual network analysis questions
A. Position (force-vector spatialization)
1. Nodes density Where are structural holes (under-populated regions)?Where are clusters an sub-clusters (over-populated regions)?Which are the largest and most cohesive clusters?
2. Relative positionWhich nodes/clusters are globally and locally central?Which nodes/clusters are global and local bridges (between clusters)?
B. Size (ranking by in-degree / out-degree)
3. Nodes connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
Main cluster and structural holes
Sub-clusters
Modularity
Visual network analysis questions
A. Position (force-vector spatialization)
1. Nodes density Where are structural holes (under-populated regions)?Where are clusters an sub-clusters (over-populated regions)?Which are the largest and most cohesive clusters?
2. Relative positionWhich nodes/clusters are globally and locally central?Which nodes/clusters are global and local bridges (between clusters)?
B. Size (ranking by in-degree / out-degree)
3. Nodes connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
Central nodes and clusters
Bridging nodes and clusters
Visual network analysis questions
A. Position (force-vector spatialization)
1. Nodes density Where are structural holes (under-populated regions)?Where are clusters an sub-clusters (over-populated regions)?Which are the largest and most cohesive clusters?
2. Relative positionWhich nodes/clusters are globally and locally central?Which nodes/clusters are global and local bridges (between clusters)?
B. Size (ranking by in-degree / out-degree)
3. Nodes connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
Authorities
Hubs
Visual network analysis questions
A. Position (force-vector spatialization)
1. Nodes density Where are structural holes (under-populated regions)?Where are clusters an sub-clusters (over-populated regions)?Which are the largest and most cohesive clusters?
2. Relative positionWhich nodes/clusters are globally and locally central?Which nodes/clusters are global and local bridges (between clusters)?
B. Size (ranking by in-degree / out-degree)
3. Nodes connectivityWhich nodes are the authorities (receive most connections)?Which nodes are the hub (originate most connections)?
C. Color (color by partition)
4. DistributionIs typology coherent with topology (partitions coincide with clusters)?Which are the exceptions (‘misplaced nodes’)?
Typology and topology
Typology and topology
Exceptions
Visual network analysis
Visual network analysis
Venturini, T., Jacomy, M and De Carvalho
Pereira, D.
Visual Network Analysis:
The example of the rio+20 online debate
(working paper)
