Alignment and integration of complex networks by hypergraph-based spectral clustering
Alignment and integration of complex networks by hypergraph-based spectral clustering
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DOI：
10.1103/physreve.86.056111
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发表时间：
2012-11-26
影响因子：
2.4
通讯作者：
Nachtergaele, Bruno
中科院分区：
文献类型：
--
作者：
Michoel, Tom;Nachtergaele, Bruno
复杂网络具有丰富的多尺度结构，反映了它们所建模的系统的动态和功能组织。通常需要同时分析多个网络，通过不止一种类型的交互来对系统建模，或者超越简单的成对交互，但目前缺乏解决这些问题的理论和计算方法。在这里，我们介绍了一个框架，用于在这样的系统中使用超图表示进行聚类和社区检测。我们的主要结果是Perron-Frobenius定理的推广，由此我们得到了有向超图和无向超图的谱聚类算法。我们通过应用程序说明我们的方法，用于多个物种之间蛋白质-蛋白质相互作用网络的局部和全球比对，用于大众分类中的三方群落检测，以及用于检测有向网络中重叠的调控路径簇。
Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.