Adaptive Dynamics of Realistic Small-World Networks

Paper in proceeding, 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.