抽样模体约束网络集成
Sampling motif-constrained ensembles of networks
摘要：
网络特性的统计显著性取决于满足特定特性但在其他方面是随机的零模型。指数随机图模型是生成这种约束集合的原则性理论框架，但由于模型不一致，或者由于不可能从中对网络进行采样，因此在实践中经常失败。这些问题影响了网络的重要情况下，规定的聚类系数或小连通子图（图案）的数量。在本文中，我们使用的Wang-Landau方法，以获得一个多典型的采样，克服了这两个问题。我们的样本，在多项式时间内，网络工程与任意程度的序列，从合奏与强加的图案计数。将这种方法应用于社交网络，我们研究了传递性和同质性之间的关系，我们量化了不同类型的基序之间的相关性，发现单个基序可以解释高达60%的基序配置文件的变化。
英文摘要：
The statistical significance of network properties is conditioned on null models which satisfy spec- ified properties but that are otherwise random. Exponential random graph models are a principled theoretical framework to generate such constrained ensembles, but which often fail in practice, either due to model inconsistency, or due to the impossibility to sample networks from them. These problems affect the important case of networks with prescribed clustering coefficient or number of small connected subgraphs (motifs). In this paper we use the Wang-Landau method to obtain a multicanonical sampling that overcomes both these problems. We sample, in polynomial time, net- works with arbitrary degree sequences from ensembles with imposed motifs counts. Applying this method to social networks, we investigate the relation between transitivity and homophily, and we quantify the correlation between different types of motifs, finding that single motifs can explain up to 60% of the variation of motif profiles.
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。
