Large scale graph completion
Abstract/Contents
- 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.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2014 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bosagh Zadeh, Reza |
|---|---|
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
| Primary advisor | Carlsson, G. (Gunnar), 1952- |
| Thesis advisor | Carlsson, G. (Gunnar), 1952- |
| Thesis advisor | Goel, Ashish |
| Thesis advisor | Leskovec, Jurij |
| Advisor | Goel, Ashish |
| Advisor | Leskovec, Jurij |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Reza Bosagh Zadeh. |
|---|---|
| Note | Submitted to the Institute for Computational and Mathematical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2014. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2014 by Reza Bosagh Zadeh
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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