Adaptive Dynamics of Realistic Small-World Networks
Paper in proceedings, 2009

Continuing in the steps of Jon Kleinberg’s and others celebrated work on decentralized search, we conduct an experimental analysis of destination sam- pling, a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic net- works with synthetic test data and with real world data, even when vertices are unevenly and non-homogeneously distributed. We investigate the same algorithm in the case where some vertices are more popular destinations for searches than others, for example obeying power-laws. We find that the algorithm adapts and adjusts the networks ac- cording to the distributions, leading to improved per- formance. The ability of the dynamic process to adapt and create small worlds in such diverse settings suggests a possible mechanism by which such networks appear in nature.


adaptive search


social networks


Oskar Sandberg

University of Gothenburg

Chalmers, Mathematical Sciences

Vilhelm Verendel

Chalmers, Energy and Environment, Physical Resource Theory

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

European Conference on Complex Systems 2009


Subject Categories

Computer and Information Science

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