Treffer: New results for finding common neighborhoods in massive graphs in the data stream model
Title:
New results for finding common neighborhoods in massive graphs in the data stream model
Authors:
Source:
Theoretical computer science. 407(1-3):302-309
Publisher Information:
Amsterdam: Elsevier, 2008.
Publication Year:
2008
Physical Description:
print, 24 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Combinatoire. Structures ordonnées, Combinatorics. Ordered structures, Combinatoire, Combinatorics, Théorie des graphes, Graph theory, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Recherche information. Graphe, Information retrieval. Graph, Divers, Miscellaneous, Borne inférieure, Lower bound, Cota inferior, Communication, Comunicación, Complexité communication, Communication complexity, Estimation erreur, Error estimation, Estimación error, Informatique théorique, Computer theory, Informática teórica, Ordinateur, Computer, Computadora, Théorie graphe, Graph theory, Teoría grafo, 05Cxx, 68R10, 68Wxx, 68XX, Algorithme graphe, Graph algorithm, Modèle calcul, Voisinage, Extremal graph theory, Graph algorithms for data streams, Space lower bounds
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
University of Palermo, Dipartimento di Matematica ed Applicazioni, Via Archirafi 34, 90123 Palermo, Italy
Computer and Automation Research Institute of the Hungarian Academy of Sciences (MTA SZTAKI), P.O. Box 63, Budapest, 1518, Hungary
Department of Algebra, Budapest University of Technology and Economics, P.O. Box 91, Budapest, 1521, Hungary
Computer and Automation Research Institute of the Hungarian Academy of Sciences (MTA SZTAKI), P.O. Box 63, Budapest, 1518, Hungary
Department of Algebra, Budapest University of Technology and Economics, P.O. Box 91, Budapest, 1521, Hungary
ISSN:
0304-3975
Rights:
Copyright 2008 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Mathematics
Mathematics
Accession Number:
edscal.20797088
Database:
PASCAL Archive
Weitere Informationen
We consider the problem of finding pairs of vertices that share large common neighborhoods in massive graphs. We give lower bounds for randomized, two-sided error algorithms that solve this problem in the data-stream model of computation. Our results correct and improve those of Buchsbaum, Giancarlo, and Westbrook [On finding common neighborhoods in massive graphs, Theoretical Computer Science, 299 (1-3) 707-718 (2004)].