Finding Supported Paths in Heterogeneous Networks

Subnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input n...

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Main Authors: Guillaume Fertin, Christian Komusiewicz, Hafedh Mohamed-Babou, Irena Rusu
Format: Article
Language:English
Published: MDPI AG 2015-10-01
Series:Algorithms
Subjects:
Online Access:http://www.mdpi.com/1999-4893/8/4/810
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spelling doaj-9c51d26d6dd34436bfa61d55b61aa5e72020-11-24T22:51:11ZengMDPI AGAlgorithms1999-48932015-10-018481083110.3390/a8040810a8040810Finding Supported Paths in Heterogeneous NetworksGuillaume Fertin0Christian Komusiewicz1Hafedh Mohamed-Babou2Irena Rusu3LINA, UMR CNRS 6241, Université de Nantes, Nantes 44322, FranceInstitut für Softwaretechnik und Theoretische Informatik, Technische Universität Berlin, Berlin D-10587, GermanyLINA, UMR CNRS 6241, Université de Nantes, Nantes 44322, FranceLINA, UMR CNRS 6241, Université de Nantes, Nantes 44322, FranceSubnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input network. We study the case that the input consists of a directed graph D and an undirected graph G on the same vertex set, and the sought pattern is a path P in D whose vertex set induces a connected subgraph of G. In this context, three concrete problems arise, depending on whether the existence of P is questioned or whether the length of P is to be optimized: in that case, one can search for a longest path or (maybe less intuitively) a shortest one. These problems have immediate applications in biological networks and predictable applications in social, information and communication networks. We study the classic and parameterized complexity of the problem, thus identifying polynomial and NP-complete cases, as well as fixed-parameter tractable and W[1]-hard cases. We also propose two enumeration algorithms that we evaluate on synthetic and biological data.http://www.mdpi.com/1999-4893/8/4/810NP-hard problemsdirected acyclic graphslongest path problemshortest path problemprotein interaction networksmetabolic networks
collection DOAJ
language English
format Article
sources DOAJ
author Guillaume Fertin
Christian Komusiewicz
Hafedh Mohamed-Babou
Irena Rusu
spellingShingle Guillaume Fertin
Christian Komusiewicz
Hafedh Mohamed-Babou
Irena Rusu
Finding Supported Paths in Heterogeneous Networks
Algorithms
NP-hard problems
directed acyclic graphs
longest path problem
shortest path problem
protein interaction networks
metabolic networks
author_facet Guillaume Fertin
Christian Komusiewicz
Hafedh Mohamed-Babou
Irena Rusu
author_sort Guillaume Fertin
title Finding Supported Paths in Heterogeneous Networks
title_short Finding Supported Paths in Heterogeneous Networks
title_full Finding Supported Paths in Heterogeneous Networks
title_fullStr Finding Supported Paths in Heterogeneous Networks
title_full_unstemmed Finding Supported Paths in Heterogeneous Networks
title_sort finding supported paths in heterogeneous networks
publisher MDPI AG
series Algorithms
issn 1999-4893
publishDate 2015-10-01
description Subnetwork mining is an essential issue in the analysis of biological, social and communication networks. Recent applications require the simultaneous mining of several networks on the same or a similar vertex set. That is, one searches for subnetworks fulfilling different properties in each input network. We study the case that the input consists of a directed graph D and an undirected graph G on the same vertex set, and the sought pattern is a path P in D whose vertex set induces a connected subgraph of G. In this context, three concrete problems arise, depending on whether the existence of P is questioned or whether the length of P is to be optimized: in that case, one can search for a longest path or (maybe less intuitively) a shortest one. These problems have immediate applications in biological networks and predictable applications in social, information and communication networks. We study the classic and parameterized complexity of the problem, thus identifying polynomial and NP-complete cases, as well as fixed-parameter tractable and W[1]-hard cases. We also propose two enumeration algorithms that we evaluate on synthetic and biological data.
topic NP-hard problems
directed acyclic graphs
longest path problem
shortest path problem
protein interaction networks
metabolic networks
url http://www.mdpi.com/1999-4893/8/4/810
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