Almost exact recovery in noisy semi-supervised learning

Avrachenkov, Konstantin; Dreveton, Maximilien

doi:10.48550/arxiv.2007.14717

Cited by 1 publication

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“…Proposition 5.1 (Adapted from Avrachenkov and Dreveton, 2020). Let A be the adjacency matrix of a homogeneous Poisson SBM as in (2.7), with ω in > ω out and s be an oracle information, defined by (5.11).…”

Section: Map Estimator For Dc-sbm With a Noisy Oraclementioning

confidence: 99%

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Statistical Analysis of Networks

Avrachenkov

Dreveton

2022

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This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Many fundamental modern tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms.Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to the statistical approach to the analysis of complex networks.In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition "à la carte". Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also useful to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7.As prerequisites for reading our book, we expect basic knowledge in probability, linear algebra and elementary notions of graph theory. We have also added appendices describing some required notions from the above mentioned disciplines.

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Section: Map Estimator For Dc-sbm With a Noisy Oraclementioning

confidence: 99%

“…Theorem 5.5 (Avrachenkov and Dreveton, 2020). Consider a DC-SBM with a noisy oracle as defined in (5.20),(5.11).…”

Section: Upper Bound On the Number Of Misclassified Nodesmentioning

confidence: 99%