社交网络的无分布模型
Distribution-Free Models of Social Networks
摘要：
大规模社交网络的结构主要是使用生成模型（generative models）来阐述的，生成模型是一种平均案例分析的形式。这一章调查了最近提出的更强大的网络模型。这些模型具有确定性和经验支持的组合结构，而不是一个特定的概率分布。我们讨论了这些模型的正式定义，以及它们如何与社交网络中的经验观察，以及相应的图形类的已知结构和算法结果。
英文摘要：
The structure of large-scale social networks has predominantly been articulated using generative models, a form of average-case analysis. This chapter surveys recent proposals of more robust models of such networks. These models posit deterministic and empirically supported combinatorial structure rather than a specific probability distribution. We discuss the formal definitions of these models and how they relate to empirical observations in social networks, as well as the known structural and algorithmic results for the corresponding graph classes.
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。
