title: Alignment and integration of complex networks by hypergraph-based spectral clustering.
abstract: 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.
authors.affiliation: Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Albertstrasse 19, D-79104 Freiburg, Germany. tom.michoel@roslin.ed.ac.uk
authors.forename: Tom
authors.initials: T
authors.lastname: Michoel
authors.forename: Bruno
authors.initials: B
authors.lastname: Nachtergaele
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome