现实小世界网络的自适应动力学
Adaptive Dynamics of Realistic Small-World Networks
摘要：
继续Jon Kleinberg和其他人在小世界网络中分散搜索的着名工作，我们对产生小世界网络的动态算法进行了实验分析。我们发现，该算法鲁棒地适应各种各样的情况下，在现实的地理网络与合成测试数据和现实世界的数据，即使顶点是不均匀和非均匀分布。我们调查相同的算法的情况下，一些顶点是更受欢迎的目的地比其他人的搜索，例如遵守幂律。我们发现，该算法适应和调整网络根据分布，从而提高性能。动态过程在如此多样化的环境中适应和创造小世界的能力表明了这种网络在自然界中出现的可能机制。
英文摘要：
Continuing in the steps of Jon Kleinberg's and others celebrated work on decentralized search in small-world networks, we conduct an experimental analysis of a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic networks with synthetic test data and with real world data, even when vertices are uneven 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 according to the distributions, leading to improved performance. 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.
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