Finding reliable subgraphs from large probabilistic graphs

Näytä kaikki kuvailutiedot



Pysyväisosoite

http://hdl.handle.net/10138/143972

Lähdeviite

Hintsanen , P & Toivonen , H 2008 , ' Finding reliable subgraphs from large probabilistic graphs ' , Data Mining and Knowledge Discovery , vol. 17 , no. 1 , pp. 3-23 . https://doi.org/10.1007/s10618-008-0106-1

Julkaisun nimi: Finding reliable subgraphs from large probabilistic graphs
Tekijä: Hintsanen, Petteri; Toivonen, Hannu
Tekijän organisaatio: COMBI TUTKIJAKOULU (-2009)
Helsinki Institute for Information Technology HIIT (-2009)
Finnish Centre of Excellence in Algorithmic Data Analysis Research (Algodan)
Department of Computer Science
Discovery Research Group/Prof. Hannu Toivonen
Päiväys: 2008
Kieli: eng
Sivumäärä: 21
Kuuluu julkaisusarjaan: Data Mining and Knowledge Discovery
ISSN: 1384-5810
DOI-tunniste: https://doi.org/10.1007/s10618-008-0106-1
URI: http://hdl.handle.net/10138/143972
Tiivistelmä: Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.Reliable subgraphs can be used, for example, to find and rank nontrivial links between given vertices, to concisely visualize large graphs, or to reduce the size of input for computationally demanding graph algorithms. We propose two new heuristics for solving the most reliable subgraph extraction problem on large, undirected probabilistic graphs. Such a problem is specified by a probabilistic graph G subject to random edge failures, a set of terminal vertices, and an integer K. The objective is to remove K edges from G such that the probability of connecting the terminals in the remaining subgraph is maximized. We provide some technical details and a rough analysis of the proposed algorithms. The practical performance of the methods is evaluated on real probabilistic graphs from the biological domain. The results indicate that the methods scale much better to large input graphs, both computationally and in terms of the quality of the result.
Avainsanat: 113 Computer and information sciences
Vertaisarvioitu: Kyllä
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: acceptedVersion


Tiedostot

Latausmäärä yhteensä: Ladataan...

Tiedosto(t) Koko Formaatti Näytä
dmkd08.pdf 381.9KB PDF Avaa tiedosto

Viite kuuluu kokoelmiin:

Näytä kaikki kuvailutiedot