Network clustering and graph cluster randomization
Abstract/Contents
- Abstract
- Networks are a fundamental tool to model complex systems from diverse domains. Clustering is a prominent attribute of real-world networks, which underpins many ideas in the theory and methodology research as well as applications. This dissertation consists three recent works in this direction. For theoretical understanding, we propose new metrics to quantify the level of network clustering, and develop new theory on the existence of network community structure from the local triadic closure phenomenon. For methodology, we propose the first motif-aware local graph clustering algorithm to detect the community structure on a given network. This algorithm enjoys both the improved performance of motif-based clustering methods and scalability of local clustering algorithms. In application, we propose a new framework for the causal inference of A/B testing in the presence of network interference, which utilizes randomized graph clustering algorithms. We show that, using random graph clusterings in the design and analysis of experiments can significantly reduce the variance in estimating the global average treatment effect
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Yin, Hao |
|---|---|
| Degree supervisor | Lai, T. L |
| Thesis advisor | Lai, T. L |
| Thesis advisor | Benson, Austin |
| Thesis advisor | Ugander, Johan |
| Degree committee member | Benson, Austin |
| Degree committee member | Ugander, Johan |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Hao Yin |
|---|---|
| Note | Submitted to the Institute for Computational & Mathematical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Hao Yin
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...