Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.
PLoS One. 2010 Sep 2;5(9):e12528. doi: 10.1371/journal.pone.0012528.
Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult.
METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine persvasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics.
CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.
网络社区有助于复杂网络的功能组织和演化。然而，开发一种快速而准确的方法来提供异构网络的重叠模块和划分，已被证明是相当困难的。
方法/主要发现：在这里，我们引入了 ModuLand 的新概念，这是一种综合方法家族，将重叠网络模块确定为基于影响函数的中心性社区景观的山丘，并包括几个广泛使用的模块化方法作为特例。作为方法家族的各种改编，我们开发了几种算法，可有效分析加权和有向网络，并（1）以高分辨率确定普遍重叠的模块；（2）揭示允许对大型网络进行高效、缩放分析的详细层次网络结构；（3）允许确定关键网络节点；（4）有助于预测网络动态。
结论/意义：该概念为开发新方法和应用开辟了广泛的可能性，包括网络路由、分类、比较和预测。
