Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems
Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems
复制标题
DOI：
10.1103/physrevx.5.011027
复制
发表时间：
2015-03-06
影响因子：
12.5
通讯作者：
Rosvall, Martin
中科院分区：
文献类型：
--
作者：
De Domenico, Manlio;Lancichinetti, Andrea;Rosvall, Martin
为了理解社会科学和自然科学中相互关联的系统，研究人员开发了许多强大的方法来识别功能模块。例如，随着交互数据聚集到单个网络层，基于流的方法已被证明对于识别加权和定向网络中捕获流过程约束的模块化动态很有用。然而，许多相互连接的系统由代理或组件组成，这些代理或组件表现出多层交互，可能来自几个不同的过程。不可避免地，将这个错综复杂的网络表示为单个聚合网络会导致信息丢失，并可能会模糊实际组织。这里，我们提出了一种基于网络流压缩的方法，该方法可以识别非聚合多层网络中层内和层间的模块化流。我们在合成多层网络上的数值实验表明，在聚合网络或单独处理层时，分析是失败的，而多层方法可以准确地识别来自相同交互过程的跨层模块。我们利用我们的发现，揭示了两个以主题为层的多层合作网络的社区结构：隶属于Pierre Auger天文台的科学家和在arxiv上发表网络工作的科学家。与传统的聚合方法相比，多层方法揭示了相互关联的主题，并揭示了具有更多重叠的较小模块，从而更好地捕获了实际组织。
To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.