基于有效社区检测的网络隐藏信息提取
Extraction of hidden information by efficient community detection in networks
摘要：
目前，我们被大量的实验数据淹没，网络物理学有可能成为一种宝贵的方法，以增加我们对大型交互数据集的理解。然而，由于两个原因，这种潜力通常没有实现：发现网络的隐藏社区结构，称为社区检测，是困难的，而且，即使人们对这种社区结构有一个想法，如何有效地使用这些信息也不是先验的。在这里，为了解决这两个问题，我们，首先，确定最佳社区结构的给定网络的模块化，利用最近推出的社区检测方法。其次，我们开发了一种方法来使用这些社区信息从网络中提取隐藏的信息。当应用于蛋白质-蛋白质相互作用网络时，所提出的方法优于仅使用网络的局部信息的当前最先进的方法。该方法一般适用于许多地区的网络。
英文摘要：
Currently, we are overwhelmed by a deluge of experimental data, and network physics has the potential to become an invaluable method to increase our understanding of large interacting datasets. However, this potential is often unrealized for two reasons: uncovering the hidden community structure of a network, known as community detection, is difficult, and further, even if one has an idea of this community structure, it is not a priori obvious how to efficiently use this information. Here, to address both of these issues, we, first, identify optimal community structure of given networks in terms of modularity by utilizing a recently introduced community detection method. Second, we develop an approach to use this community information to extract hidden information from a network. When applied to a protein-protein interaction network, the proposed method outperforms current state-of-the-art methods that use only the local information of a network. The method is generally applicable to networks from many areas.
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。
