Book contents
- Frontmatter
- Contents
- Preface
- List of Contributors
- 1 Introduction
- Part One Refinements of Worst-Case Analysis
- Part Two Deterministic Models of Data
- Part Three Semirandom Models
- 8 Distributional Analysis
- 9 Introduction to Semirandom Models
- 10 Semirandom Stochastic Block Models
- 11 Random-Order Models
- 12 Self-Improving Algorithms
- Part Four Smoothed Analysis
- Part Five Applications in Machine Learning and Statistics
- Part Six Further Applications
- Index
10 - Semirandom Stochastic Block Models
from Part Three - Semirandom Models
Published online by Cambridge University Press: 17 December 2020
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Book contents
- Frontmatter
- Contents
- Preface
- List of Contributors
- 1 Introduction
- Part One Refinements of Worst-Case Analysis
- Part Two Deterministic Models of Data
- Part Three Semirandom Models
- 8 Distributional Analysis
- 9 Introduction to Semirandom Models
- 10 Semirandom Stochastic Block Models
- 11 Random-Order Models
- 12 Self-Improving Algorithms
- Part Four Smoothed Analysis
- Part Five Applications in Machine Learning and Statistics
- Part Six Further Applications
- Index
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- Beyond the Worst-Case Analysis of Algorithms , pp. 212 - 233Publisher: Cambridge University PressPrint publication year: 2021