揭示网络分区之间的共识和分歧
Revealing consensus and dissensus between network partitions
摘要：
社区检测方法试图将网络划分为具有相似属性的节点组，从而揭示其大尺度结构。采用这种方法的一个主要挑战是，它们往往是退化的，通常会产生一个复杂的景观竞争的答案。为了从一群可供选择的解决方案中提取理解，存在许多方法以总结整个分布的单一分区“点估计”的形式在它们之间建立共识。在这里，我们表明，它是在一般情况下，不可能从这样的点估计得到一致的答案时，潜在的分布是太异质。作为一种替代方案，我们提供了一套全面的方法，旨在表征和总结复杂的人口分区的方式，不仅捕捉现有的共识，但人口的元素之间的纠纷。我们的方法是能够模拟多个共识可以共存的分区混合人口，代表不同的竞争假设的网络结构。我们还展示了我们的方法如何可以用来比较成对的分区，它们如何可以推广到层次划分，并被用来执行竞争假设之间的统计模型选择。
英文摘要：
Community detection methods attempt to divide a network into groups of nodes that share similar properties, thus revealing its large-scale structure. A major challenge when employing such methods is that they are often degenerate, typically yielding a complex landscape of competing answers. As an attempt to extract understanding from a population of alternative solutions, many methods exist to establish a consensus among them in the form of a single partition "point estimate" that summarizes the whole distribution. Here we show that it is in general not possible to obtain a consistent answer from such point estimates when the underlying distribution is too heterogeneous. As an alternative, we provide a comprehensive set of methods designed to characterize and summarize complex populations of partitions in a manner that captures not only the existing consensus, but also the dissensus between elements of the population. Our approach is able to model mixed populations of partitions where multiple consensuses can coexist, representing different competing hypotheses for the network structure. We also show how our methods can be used to compare pairs of partitions, how they can be generalized to hierarchical divisions, and be used to perform statistical model selection between competing hypotheses.
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。
