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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: Thesis (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...
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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.

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