标题： 历史偶然性引导随机组装图的拓扑结构 显示英文标题

标题： Historical Contingencies Steer the Topology of Randomly Assembled Graphs

摘要： 图被用于表示和分析从物理学、生物学、化学、行星科学到社会科学等不同领域的数据。 在各个领域中，随机图模型将生成过程与期望的图属性联系起来，并允许从不同的集合中进行采样。 在这里，我们介绍了一个新的随机图模型，该模型受组装理论的启发，并描述了它生成的图。 我们证明，使用我们方法生成的图代表了一个多样化的集合，其特征是广泛的总结统计量，甚至在度序列相同的图中也是出乎意料的。 最后，我们展示了这些图的独特性质是由生成过程中的历史偶然性所促成的。 这些结果为基于组装理论的新型采样方法的进一步发展奠定了基础，并可用于药物发现和材料科学。 显示英文摘要

摘要： Graphs are used to represent and analyze data in domains as diverse as physics, biology, chemistry, planetary science, and the social sciences. Across domains, random graph models relate generative processes to expected graph properties, and allow for sampling from distinct ensembles. Here we introduce a new random graph model, inspired by assembly theory, and characterize the graphs it generates. We show that graphs generated using our method represent a diverse ensemble, characterized by a broad range of summary statistics, unexpected even in graphs with identical degree sequences. Finally we demonstrate that the distinct properties of these graphs are enabled by historical contingencies during the generative process. These results lay the foundation for further development of novel sampling methods based on assembly theory with applications to drug discovery and materials science.