skip to content
Large scale graph completion Preview this item
ClosePreview this item
Checking...

Large scale graph completion

Author: Reza Bosagh Zadeh; G Carlsson; Ashish Goel; Jurij Leskovec; Stanford University. Institute for Computational and Mathematical Engineering.
Publisher: 2014.
Dissertation: Ph. D. Stanford University 2014
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Database:WorldCat
Summary:
We present a framework for completing missing edges in a large graph. We focus on each component of the framework separately, provide algorithms, prove efficiency guarantees, and run experiments. The system described is partially in production at the Twitter web service. In the first chapter we describe a method to compute similar nodes in the graph, given a sparsity assumption. In the second chapter, we describe a  Read more...
Rating:

(not yet rated) 0 with reviews - Be the first.

 

Find a copy online

Links to this item

Find a copy in the library

&AllPage.SpinnerRetrieving; Finding libraries that hold this item...

Details

Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Reza Bosagh Zadeh; G Carlsson; Ashish Goel; Jurij Leskovec; Stanford University. Institute for Computational and Mathematical Engineering.
OCLC Number: 869851465
Notes: Submitted to the Institute for Computational and Mathematical Engineering.
Description: 1 online resource
Responsibility: Reza Bosagh Zadeh.

Abstract:

We present a framework for completing missing edges in a large graph. We focus on each component of the framework separately, provide algorithms, prove efficiency guarantees, and run experiments. The system described is partially in production at the Twitter web service. In the first chapter we describe a method to compute similar nodes in the graph, given a sparsity assumption. In the second chapter, we describe a generalization of the first chapter to compute singular values of a very tall and skinny matrix. Such matrices are so large that they cannot even be streamed through a single machine. In the final chapter, we develop a novel machine learning algorithm to learn weights on a random walk, while also modeling edge removals.

Reviews

User-contributed reviews
Retrieving GoodReads reviews...
Retrieving DOGObooks reviews...

Tags

Be the first.
Confirm this request

You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.

Linked Data


Primary Entity

<http://www.worldcat.org/oclc/869851465> # Large scale graph completion
    a bgn:Thesis, schema:CreativeWork, pto:Web_document, schema:Book, schema:MediaObject ;
   bgn:inSupportOf "" ;
   library:oclcnum "869851465" ;
   schema:contributor <http://viaf.org/viaf/21677617> ; # Ashish Goel
   schema:contributor <http://experiment.worldcat.org/entity/work/data/1783159461#Person/leskovec_jurij> ; # Jurij Leskovec
   schema:contributor <http://viaf.org/viaf/264754962> ; # Stanford University. Institute for Computational and Mathematical Engineering.
   schema:contributor <http://viaf.org/viaf/21239625> ; # Gunnar Carlsson
   schema:creator <http://experiment.worldcat.org/entity/work/data/1783159461#Person/bosagh_zadeh_reza> ; # Reza Bosagh Zadeh
   schema:datePublished "2014" ;
   schema:description "We present a framework for completing missing edges in a large graph. We focus on each component of the framework separately, provide algorithms, prove efficiency guarantees, and run experiments. The system described is partially in production at the Twitter web service. In the first chapter we describe a method to compute similar nodes in the graph, given a sparsity assumption. In the second chapter, we describe a generalization of the first chapter to compute singular values of a very tall and skinny matrix. Such matrices are so large that they cannot even be streamed through a single machine. In the final chapter, we develop a novel machine learning algorithm to learn weights on a random walk, while also modeling edge removals."@en ;
   schema:exampleOfWork <http://worldcat.org/entity/work/id/1783159461> ;
   schema:inLanguage "en" ;
   schema:name "Large scale graph completion"@en ;
   schema:productID "869851465" ;
   schema:publication <http://www.worldcat.org/title/-/oclc/869851465#PublicationEvent/2014> ;
   schema:url <http://purl.stanford.edu/xn594ph6993> ;
   wdrs:describedby <http://www.worldcat.org/title/-/oclc/869851465> ;
    .


Related Entities

<http://experiment.worldcat.org/entity/work/data/1783159461#Person/bosagh_zadeh_reza> # Reza Bosagh Zadeh
    a schema:Person ;
   schema:familyName "Bosagh Zadeh" ;
   schema:givenName "Reza" ;
   schema:name "Reza Bosagh Zadeh" ;
    .

<http://experiment.worldcat.org/entity/work/data/1783159461#Person/leskovec_jurij> # Jurij Leskovec
    a schema:Person ;
   schema:familyName "Leskovec" ;
   schema:givenName "Jurij" ;
   schema:name "Jurij Leskovec" ;
    .

<http://viaf.org/viaf/21239625> # Gunnar Carlsson
    a schema:Person ;
   schema:birthDate "1952" ;
   schema:familyName "Carlsson" ;
   schema:givenName "Gunnar" ;
   schema:givenName "G." ;
   schema:name "Gunnar Carlsson" ;
    .

<http://viaf.org/viaf/21677617> # Ashish Goel
    a schema:Person ;
   schema:familyName "Goel" ;
   schema:givenName "Ashish" ;
   schema:name "Ashish Goel" ;
    .

<http://viaf.org/viaf/264754962> # Stanford University. Institute for Computational and Mathematical Engineering.
    a schema:Organization ;
   schema:name "Stanford University. Institute for Computational and Mathematical Engineering." ;
    .


Content-negotiable representations

Close Window

Please sign in to WorldCat 

Don't have an account? You can easily create a free account.