|Résumé||I will present two results in complex networks.
The first one is a generic active learning method, applicable to networks
arising in various fields. In many networks, vertices have hidden attributes
that are correlated with the network’s topology. For instance, in social
networks, people are more likely to be friends if they are demographically
similar. In food webs, predators typically eat prey of lower body mass.
We explore a setting in which the network’s topology is known, but these
attributes are not. If each vertex can be queried, learning the value of its
hidden attributes —but only at some cost— then we need an algorithm which
chooses which vertex to query next, in order to learn as much as possible about
the attributes of the remaining vertices. We present two heuristics and assess
The second one is an application of complex network methods to epistemology.
Thanks to a large database of 200K articles, we empirically study the “complex
systems” field and its claims to find universal principles applying to systems
in general. Study of the references shared by the papers allows us to obtain a
global point of view on the structure of this highly interdisciplinary field. We
show that its overall coherence does not arise from a universal theory but
instead from computational techniques and fruitful adaptations of the idea of
self-organization to specific systems. At a more local level, specifically
interdisciplinary, we find that understanding between vastly different
scientific cultures is possible thanks to “trading zones”, i.e. sub-communities
that manage to work at the interface around specific tools (e.g. a DNA
microchip) or concepts (a network). |