Stochastic block model, the Glossary

Table of Contents

1. 20 relations: Belief propagation, Blockmodeling, Categorical distribution, Community structure, Erdős–Rényi model, Estimator, Generative model, Graph (discrete mathematics), Machine learning, Maximum likelihood estimation, Network science, NP-completeness, Paul W. Holland, Percolation threshold, Regularization (mathematics), Semidefinite programming, Social Networks (journal), Spectral clustering, Statistics, Topic model.

2. Blockmodeling
3. Random graphs

Belief propagation

Belief propagation, also known as sum–product message passing, is a message-passing algorithm for performing inference on graphical models, such as Bayesian networks and Markov random fields.

See Stochastic block model and Belief propagation

Blockmodeling

Blockmodeling is a set or a coherent framework, that is used for analyzing social structure and also for setting procedure(s) for partitioning (clustering) social network's units (nodes, vertices, actors), based on specific patterns, which form a distinctive structure through interconnectivity. Stochastic block model and Blockmodeling are random graphs.

See Stochastic block model and Blockmodeling

Categorical distribution

In probability theory and statistics, a categorical distribution (also called a generalized Bernoulli distribution, multinoulli distribution) is a discrete probability distribution that describes the possible results of a random variable that can take on one of K possible categories, with the probability of each category separately specified.

See Stochastic block model and Categorical distribution

In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. Stochastic block model and community structure are networks.

See Stochastic block model and Community structure

Erdős–Rényi model

In the mathematical field of graph theory, the Erdős–Rényi model refers to one of two closely related models for generating random graphs or the evolution of a random network. Stochastic block model and Erdős–Rényi model are random graphs.

See Stochastic block model and Erdős–Rényi model

Estimator

In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished.

See Stochastic block model and Estimator

Generative model

In statistical classification, two main approaches are called the generative approach and the discriminative approach.

See Stochastic block model and Generative model

Graph (discrete mathematics)

In discrete mathematics, particularly in graph theory, a graph is a structure consisting of a set of objects where some pairs of the objects are in some sense "related".

See Stochastic block model and Graph (discrete mathematics)

Machine learning

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions.

See Stochastic block model and Machine learning

Maximum likelihood estimation

In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data.

See Stochastic block model and Maximum likelihood estimation

Network science

Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges). Stochastic block model and network science are networks.

See Stochastic block model and Network science

NP-completeness

In computational complexity theory, a problem is NP-complete when.

See Stochastic block model and NP-completeness

Paul W. Holland

Paul William Holland (born 25 April 1940) is an American statistician.

See Stochastic block model and Paul W. Holland

Percolation threshold

The percolation threshold is a mathematical concept in percolation theory that describes the formation of long-range connectivity in random systems. Stochastic block model and percolation threshold are random graphs.

See Stochastic block model and Percolation threshold

Regularization (mathematics)

In mathematics, statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler".

See Stochastic block model and Regularization (mathematics)

Semidefinite programming

Semidefinite programming (SDP) is a subfield of mathematical programming concerned with the optimization of a linear objective function (a user-specified function that the user wants to minimize or maximize) over the intersection of the cone of positive semidefinite matrices with an affine space, i.e., a spectrahedron.

See Stochastic block model and Semidefinite programming

Social Networks is a quarterly peer-reviewed academic journal covering research on social network theory.

See Stochastic block model and Social Networks (journal)

Spectral clustering

In multivariate statistics, spectral clustering techniques make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions.

See Stochastic block model and Spectral clustering

Statistics

Statistics (from German: Statistik, "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.

See Stochastic block model and Statistics

Topic model

In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.

See Stochastic block model and Topic model

See also

Blockmodeling

• Andrej Mrvar
• Blockmodeling
• Blockmodeling linked networks
• Confirmatory blockmodeling
• Deterministic blockmodeling
• Exploratory blockmodeling
• Generalized blockmodeling
• Generalized blockmodeling of binary networks
• Generalized blockmodeling of valued networks
• Harrison White
• Homogeneity blockmodeling
• Implicit blockmodeling
• Stochastic block model
• Vladimir Batagelj

Random graphs

• Activity-driven model
• Autologistic actor attribute models
• Barabási–Albert model
• Bianconi–Barabási model
• Blockmodeling
• Erdős–Rényi model
• Giant component
• Lancichinetti–Fortunato–Radicchi benchmark
• Loop-erased random walk
• Maximum-entropy random graph model
• Maze generation algorithm
• Percolation critical exponents
• Percolation threshold
• Planted clique
• Rado graph
• Random cluster model
• Random geometric graph
• Random graph
• Random recursive tree
• Random regular graph
• Random tree
• Soft configuration model
• Stochastic block model
• The Strange Logic of Random Graphs
• Watts–Strogatz model

References

[1] https://en.wikipedia.org/wiki/Stochastic_block_model

Also known as Stochastic blockmodeling.