Alignment and integration of complex networks by hypergraph-based spectral clustering

Complex networks possess a rich, multi-scale 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.Comment: 16 pages, 5 figures; revised version with minor corrections and figures printed in two-column format for better readability; algorithm implementation and supplementary information available at Google code at http://schype.googlecode.co

Transversals of subtree hypergraphs and the source location problem in hypergraphs

A hypergraph $H=(V,E)$ is a subtree hypergraph if there is a tree~$T$ on~$V$ such that each hyperedge of~$E$ induces a subtree of~$T$. Since the number of edges of a subtree hypergraph can be exponential in $n=|V|$, one can not always expect to be able to find a minimum size transversal in time polynomial in~$n$. In this paper, we show that if it is possible to decide if a set of vertices $W\subseteq V$ is a transversal in time~$S(n)$ (\,where $n=|V|$\,), then it is possible to find a minimum size transversal in~$O(n^3\,S(n))$. This result provides a polynomial algorithm for the Source Location Problem\,: a set of $(k,l)$-sources for a digraph $D=(V,A)$ is a subset~$S$ of~$V$ such that for any $v\in V$ there are~$k$ arc-disjoint paths that each join a vertex of~$S$ to~$v$ and~$l$ arc-disjoint paths that each join~$v$ to~$S$. The Source Location Problem is to find a minimum size set of $(k,l)$-sources. We show that this is a case of finding a transversal of a subtree hypergraph, and that in this case~$S(n)$ is polynomial

Characterizing extremal digraphs for identifying codes and extremal cases of Bondy's theorem on induced subsets

An identifying code of a (di)graph $G$ is a dominating subset $C$ of the vertices of $G$ such that all distinct vertices of $G$ have distinct (in)neighbourhoods within $C$. In this paper, we classify all finite digraphs which only admit their whole vertex set in any identifying code. We also classify all such infinite oriented graphs. Furthermore, by relating this concept to a well known theorem of A. Bondy on set systems we classify the extremal cases for this theorem

Grassmann Integral Representation for Spanning Hyperforests

Given a hypergraph G, we introduce a Grassmann algebra over the vertex set, and show that a class of Grassmann integrals permits an expansion in terms of spanning hyperforests. Special cases provide the generating functions for rooted and unrooted spanning (hyper)forests and spanning (hyper)trees. All these results are generalizations of Kirchhoff's matrix-tree theorem. Furthermore, we show that the class of integrals describing unrooted spanning (hyper)forests is induced by a theory with an underlying OSP(1|2) supersymmetry.Comment: 50 pages, it uses some latex macros. Accepted for publication on J. Phys.

GRACE as a unifying approach to graph-transformation-based specification1 1This work was partially supported by the ESPRIT Working Group Applications of Graph Transformation (APPLIGRAPH) and the EC TMR Network GETGRATS (General Theory of Graph Transformation Systems).

AbstractIn this paper, we sketch some basic ideas and features of the graph-transformation-based specification language GRACE. The aim of GRACE is to support the modeling of a wide spectrum of graph and graphical processes in a structured and uniform way including visualization and verification