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Seminaire MASCOTTEComplex networks: measures, deterministic models, implementation
par Philippe Giabbanelli
| Date : | 16/03/10 | | Time : | 10H30 | | Location : | Galois Coriolis |
Complex networks have been a very active topic of research in the past decade. It was found that numerous networks in the real-world (e.g., protein interactions, social networks) share properties, such as a small average distance, a high clustering (i.e., probability that if 'a' is connected to 'b' and 'b' is connected to 'c' then 'a' is connected to 'c'), and a degree distribution that follows a power-law. We summarize the three main measures (i.e., average distance, clustering and degree distribution) and focus on models that generate graphs with particular values for these measures (e.g., high clustering). We quickly present stochastic models such as the Watts-Strogatz and Albert-Barabasi models, and we focus on deterministic models.
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