流压缩可以揭示网络中的重叠模块组织
Compression of Flow Can Reveal Overlapping-Module Organization in Networks
摘要：
为了更好地理解大型网络的重叠模块化组织，这里我们介绍重叠模块的映射方程。在这个信息理论框架中，我们使用压缩和规律性检测之间的对应关系。广义映射方程衡量了当我们将网络划分为可能重叠的模块时，我们可以压缩网络中流的描述的程度。当我们在重叠的网络分区上最小化广义映射方程时，我们检测捕获流的模块，并确定模块之间的边界处的哪些节点应该被分类在多个模块中以及分类到什么程度。通过一种新的贪婪搜索算法，我们发现一些网络，如C。雅利安人，是最好的描述模块占主导地位的硬边界，但其他人，例如，稀疏的欧洲道路网，有一个高度重叠的模块组织。
英文摘要：
To better understand the overlapping modular organization of large networks with respect to flow, here we introduce the map equation for overlapping modules. In this information-theoretic framework, we use the correspondence between compression and regularity detection. The generalized map equation measures how well we can compress a description of flow in the network when we partition it into modules with possible overlaps. When we minimize the generalized map equation over overlapping network partitions, we detect modules that capture flow and determine which nodes at the boundaries between modules should be classified in multiple modules and to what degree. With a novel greedy search algorithm, we find that some networks, for example, the neural network of C. Elegans, are best described by modules dominated by hard boundaries, but that others, for example, the sparse European road network, have a highly overlapping modular organization.
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。
