家系学领域快速发展，相关平台已积累数十亿条记录、覆盖数百万用户，由此产生的网络成为当前可分析的最大且最复杂的网络之一。尽管家系网络研究已取得显著进展，人类亲缘网络是否具有普适性结构特征仍不明确。为此，我们提出一种整合式家系网络分析方法，将网络理论结构与推断出的时间概念相结合。基于Kinsources知识库中百余个数据集，我们以家系学语境重新诠释标准网络测度，并引入“伪世代”（pseudogenerations）方法——一种直接从网络拓扑中提取时间结构的技术。在此框架下，我们识别出跨数据集共有的特征：家系网络呈现类无标度（scale-free–like）的度分布与连通分量规模分布、多尺度家族组织结构，以及在遗传距离和联姻距离意义上的小世界特性；二连通分量（2-components）构成家系结构的自然基本单元；网络表现出一致的异配混合（disassortative mixing）；且所记录的联姻关系相对于所有可能配对而言，显著偏向于较短的遗传距离。此外，我们还记录了时间与人口统计模式，包括所记录的父母与子女信息随时间的变化趋势，以及所记录联姻、父母与子女之间的相关性。这些结果表明，不同来源的家系数据集共享一组共通的结构与时间特征，为人类亲缘网络存在普适性特征提供了证据，并确立了一套适用于其比较分析的一般性框架。

The rapid growth of the genealogical sector, spanning platforms with billions of records and millions of users, has produced some of the largest and most complex networks available for analysis. Despite substantial advances in genealogical network research, it remains unclear whether human kinship networks exhibit universal structural properties. We address this by developing an integrated approach to genealogical network analysis that combines network-theoretic structure with an inferred notion of time. Using over one hundred datasets from the Kinsources repository, we reinterpret standard network measures in genealogical terms and introduce \emph{pseudogenerations}, a method for extracting temporal structure directly from network topology. Within this framework, we identify common features shared across datasets. We find that genealogical networks exhibit scale-free--like degree and component-size distributions, multiscale family organization, and small-world behavior with respect to genetic and union-based distances. We show that 2-components provide a natural unit of genealogical structure, observe consistent disassortative mixing, and find that recorded unions are strongly biased toward short genetic distances relative to potential pairings. We also document temporal and demographic patterns, including shifts in recorded parental and child information, as well as correlations among recorded unions, parents, and children. These results suggest that diverse genealogical datasets share a common set of structural and temporal characteristics, providing evidence for universal features of human kinship networks and establishing a general framework for their comparative analysis.