论文标题
建模网络的演变，以缩小结构多样性
Modeling the Evolution of Networks as Shrinking Structural Diversity
论文作者
论文摘要
本文根据结构多样性的概念回顾并评估网络演化的模型。我们表明，多样性是网络演变三个原则的基本主题：优先依恋模型，连接性和链接预测。我们表明，在所有三种情况下，在理论上和经验上都显而易见地缩小多样性的主要趋势是显而易见的。在以前的工作中，许多不同的数据已被建模为网络：社会结构，导航结构，传输基础架构，通信等。几乎所有这些类型的网络都不是静态结构，而是动态系统连续变化。因此，一个重要的问题涉及这些网络中可观察到的趋势及其在现有网络模型方面的解释。我们在本文中表明，大多数数值网络特征都遵循统计上显着的趋势，并且可以通过考虑多样性概念来预测这些趋势。我们的工作扩展了以前的工作，该工作观察到缩小的网络直径到诸如聚类系数，幂律指数和随机步行返回概率之类的措施，并证明优先的附件模型和链接预测算法是合理的。我们使用二十七个时间不断发展的现实世界网络数据集的多样化集合在实验中评估了我们的假设。
This article reviews and evaluates models of network evolution based on the notion of structural diversity. We show that diversity is an underlying theme of three principles of network evolution: the preferential attachment model, connectivity and link prediction. We show that in all three cases, a dominant trend towards shrinking diversity is apparent, both theoretically and empirically. In previous work, many kinds of different data have been modeled as networks: social structure, navigational structure, transport infrastructure, communication, etc. Almost all these types of networks are not static structures, but instead dynamic systems that change continuously. Thus, an important question concerns the trends observable in these networks and their interpretation in terms of existing network models. We show in this article that most numerical network characteristics follow statistically significant trends going either up or down, and that these trends can be predicted by considering the notion of diversity. Our work extends previous work observing a shrinking network diameter to measures such as the clustering coefficient, power-law exponent and random walk return probability, and justifies preferential attachment models and link prediction algorithms. We evaluate our hypothesis experimentally using a diverse collection of twenty-seven temporally evolving real-world network datasets.
