Large-scale network : a scalable learning algorithm and visualization
Name
1193029179-MIT.pdf
Size
1.61 MB
Format
Adobe PDF
Checksum (MD5)
ae8a4d6e0f7de080695ad68a82378c5c
Author(s)
Saengja, Tossaporn.
Advisor(s)
Alex "Sandy" Pentland.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
The amount of available data is predicted to be more than thousands of gigabytes per human by 2020, and current technologies are connecting people together. Data on observed actions become more available which are able to give insights on the underlying connections between individuals. However, the growing size of data presents challenges for existing machine learning methods and visualization platform. In this thesis, I focus on two problems. First, I extend an existing network learning method to large-scale networks with alternating direction method of multipliers. Testing the method with synthetic datasets, I show that the algorithm achieves similar performance with less computation time. Second, I build a tool for large-scale network data exploration. The tool is tested on several large-scale real-world datasets to illustrate its benefits.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 49-53).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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