missSBM

KO | EN

gitlite — search

#javascript #hacktoberfest #ai #golang #machine-learning #rust #docker #java #windows #awesome #cli #security #ios #open-source

(★ 13)

An R package for adjusting Stochastic Block Models from networks data sampled under various missing data conditions

missSBM 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# missSBM: Handling missing data in Stochastic Block Models

[![pkgdown](https://github.com/GrossSBM/missSBM/actions/workflows/pkgdown.yaml/badge.svg)](https://grosssbm.github.io/missSBM/)
[![R-CMD-check](https://github.com/GrossSBM/missSBM/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/GrossSBM/missSBM/actions/workflows/R-CMD-check.yaml)
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/missSBM)](https://cran.r-project.org/package=missSBM)
[![last
commit](https://img.shields.io/github/last-commit/GrossSBM/missSBM.svg)](https://github.com/GrossSBM/missSBM/commits/master)
[![Codecov test
coverage](https://codecov.io/gh/GrossSBM/missSBM/branch/master/graph/badge.svg)](https://app.codecov.io/gh/GrossSBM/missSBM?branch=master)

## Description

> When a network is partially observed (here, NAs in the adjacency
> matrix rather than 1 or 0 due to missing information between node
> pairs), it is possible to account for the underlying process that
> generates those NAs. ‘missSBM’, presented in ‘Barbillon, Chiquet and
> Tabouy’ (2022)
> [10.18637/jss.v101.i12](https://doi.org/10.18637/jss.v101.i12),
> adjusts the popular stochastic block model from network data sampled
> under various missing data conditions, as described in ‘Tabouy,
> Barbillon and Chiquet’ (2019)
> [10.1080/01621459.2018.1562934](https://doi.org/10.1080/01621459.2018.1562934).

**missSBM** covers a variety of sampling designs for the missing
dyads/nodes, split into two families depending on whether the sampling
mechanism can be ignored:

- **Missing At Random (MAR)**: `"dyad"`, `"node"`, `"covar-dyad"`,
  `"covar-node"`, `"snowball"` — whether a dyad/node is observed does
  not depend on the network itself, so a naive fit on the observed part
  alone is already consistent.
- **Missing Not At Random (MNAR)**: `"double-standard"`, `"block-node"`,
  `"block-dyad"`, `"degree"` — the sampling depends on the underlying
  block structure, the edge value, or the node degree, and must be
  modeled jointly with the network to avoid biased inference.

## Installation

The last CRAN version is available via

``` r
install.packages("missSBM")
```

The development version is available via

``` r
devtools::install_github("GrossSBM/missSBM")
```

## Illustration

We illustrate **missSBM** on the French political blogosphere network
(`frenchblog2007`, bundled with the package): about 190 blogs, edges
standing for hyperlinks, each blog labeled by political party.

### Fit a SBM on the fully observed network

``` r
library(missSBM)
library(igraph)

data(frenchblog2007)
frenchblog2007 <- delete_vertices(frenchblog2007, which(degree(frenchblog2007) == 0))
adjacency <- as_adjacency_matrix(frenchblog2007, sparse = FALSE)
```

``` r
full_fit <- estimateMissSBM(adjacency, vBlocks = 1:10, sampling = "node",
                             control = missSBM_param(trace = FALSE))
```

``` r
plot(full_fit, "icl")
```

![](https://raw.githubusercontent.com/GrossSBM/missSBM/master/man/figures/README-plot-full-1.png)

### Simulate a Missing Not At Random sampling and account for it

Real-world network data collection is rarely complete. We simulate a
**block-node** sampling design (MNAR): blogs belonging to smaller
political parties are less likely to have been surveyed than blogs in
bigger ones, a mechanism tied to the very block structure we are trying
to recover – exactly the kind of design a naive analysis of the observed
part alone would get wrong.

``` r
set.seed(1234)
block_prop <- full_fit$bestModel$fittedSBM$blockProp
sampling_rate <- ifelse(block_prop < 0.1, 0.2, 0.8)
partial_network <- observeNetwork(adjacency, sampling = "block-node",
                                   parameters = sampling_rate,
                                   clusters = full_fit$bestModel$fittedSBM$memberships)
mean(is.na(partial_network)) # fraction of missing dyads
```

[1] 0.07891381

``` r
partial_fit <- estimateMissSBM(partial_network, vBlocks = 1:10, sampling = "block-node",
                                control = missSBM_param(trace = FALSE, iterates = 2))
```

``` r
plot(partial_fit, "icl")
```

![](https://raw.githubusercontent.com/GrossSBM/missSBM/master/man/figures/README-plot-partial-1.png)

Despite about 8% of the dyads missing under this informative design,
**missSBM** recovers a block structure close to the one found on the
fully observed network (Adjusted Rand Index, the closer to 1 the
better):

``` r
aricode::ARI(full_fit$bestModel$fittedSBM$memberships, partial_fit$bestModel$fittedSBM$memberships)
```

[1] 0.8208973

``` r
par(mfrow = c(1, 2))
plot(full_fit$bestModel, "expected", dimLabels = list(row = "blogs", col = "blogs"))
```

![](https://raw.githubusercontent.com/GrossSBM/missSBM/master/man/figures/README-plot-expected-1.png)

``` r
plot(partial_fit$bestModel, "expected", dimLabels = list(row = "blogs", col = "blogs"))
```

![](https://raw.githubusercontent.com/GrossSBM/missSBM/master/man/figures/README-plot-expected-2.png)

See `vignette("case_study_war_networks")` and [the JSS
article](https://doi.org/10.18637/jss.v101.i12) for a more comprehensive
tour, including covariates and further sampling designs.

## References

Please cite our work using the following references:

Barbillon, P., Chiquet, J., & Tabouy, T. (2022). missSBM: An R Package
for Handling Missing Values in the Stochastic Block Model. *Journal of
Statistical Software*, 101(12), 1–32. DOI:
[10.18637/jss.v101.i12](https://doi.org/10.18637/jss.v101.i12)

Timothée Tabouy, Pierre Barbillon & Julien Chiquet (2019) “Variational
Inference for Stochastic Block Models from Sampled Data”, *Journal of
the American Statistical Association*, DOI:
[10.1080/01621459.2018.1562934](https://doi.org/10.1080/01621459.2018.1562934)

missSBM

missSBM 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

missSBM

missSBM 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README