节点相似性：复杂网络中连通性背后的基本原则
Node similarity as a basic principle behind connectivity in complex networks
AI总结：
本研究通过Flickr网络数据发现，节点相似性是驱动社交网络通信的关键因素，并可作为网络形成模型解释从稀疏到密集连接社会的拓扑转变。
摘要：
在高度连通的社会中，人们是如何相互联系的？由于在许多网络中可观察到幂律（无标度）节点度分布，幂律或许可被视为网络的普遍特征。但对Flickr社交在线网络中的通信进行的研究表明，幂律节点度分布仅限于稀疏连接的网络。相比之下，更密集连接的网络显示出与幂律日益增长的偏离。这项工作表明，这一观察结果与社会科学的经典观点一致，即相似性是社交网络中通信的驱动因素。通过分析Flickr网络，通信强度与节点相似性之间的强关联得到了证实。研究还表明，将节点相似性作为网络形成模型，能够重现不同网络密度的特征，因此可用来描述从弱连通社会到强连通社会的拓扑转变。
英文摘要：
How are people linked in a highly connected society? Since in many networks a power-law (scale-free) node-degree distribution can be observed, power-law might be seen as a universal characteristics of networks. But this study of communication in the Flickr social online network reveals that power-law node-degree distributions are restricted to only sparsely connected networks. More densely connected networks, by contrast, show an increasing divergence from power-law. This work shows that this observation is consistent with the classic idea from social sciences that similarity is the driving factor behind communication in social networks. The strong relation between communication strength and node similarity could be confirmed by analyzing the Flickr network. It also is shown that node similarity as a network formation model can reproduce the characteristics of different network densities and hence can be used as a model for describing the topological transition from weakly to strongly connected societies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
