# neurostatx.cli.ComputeGraphNetwork

*module*

[View source](https://github.com/gagnonanthony/NeuroStatX/blob/main/neurostatx/cli/ComputeGraphNetwork.py#L1-L1)

## neurostatx.cli.ComputeGraphNetwork.app

*attribute* · *module attribute*

```python
app = App(default_parameter=Parameter(negative=()))
```

Cyclopts application for the ComputeGraphNetwork command-line tool.

[View source](https://github.com/gagnonanthony/NeuroStatX/blob/main/neurostatx/cli/ComputeGraphNetwork.py#L20-L20)

## neurostatx.cli.ComputeGraphNetwork.ComputeGraphNetwork

*function*

```python
def ComputeGraphNetwork(in_dataset: Annotated[str, Parameter(show_default=False, group='Essential Files Options')], id_column: Annotated[str, Parameter(show_default=False, group='Essential Files Options')], desc_columns: Annotated[int, Parameter(show_default=False, group='Essential Files Options')], out_folder: Annotated[str, Parameter(show_default=True, group='Essential Files Options')] = './graph_results/', verbose: Annotated[bool, Parameter(-v, --verbose, group='Optional parameters')] = False, overwrite: Annotated[bool, Parameter(-f, --overwrite, group='Optional parameters')] = False, save_parameters: Annotated[bool, Parameter(-s, --save_parameters, group='Optional parameters')] = False, plot_distribution: Annotated[bool, Parameter(show_default=True, group='Plotting Options')] = False, import_data: Annotated[bool, Parameter(show_default=True, group='Essential Files Options')] = False, layout: Annotated[NetworkLayout, Parameter(show_default=True, show_choices=True, group='Layout Options')] = NetworkLayout.Spring, method: Annotated[str, Parameter(show_default=True, group='Layout Options')] = 'force', seed: Annotated[int, Parameter(show_default=True, group='Layout Options')] = 42, weight: Annotated[str, Parameter(show_default=True, group='Layout Options')] = 'membership')
```

Build an undirected weighted graph from a fuzzy membership matrix.

ComputeGraphNetwork is designed to work seamlessly with
[FuzzyClustering][neurostatx.cli.FuzzyClustering.FuzzyClustering]. Mapping
membership matrices to a graph allows later use of graph-theory statistics
such as shortest path and betweenness centrality.

> **Notes**
>
> **Layout algorithms**
> 
> Node positions are determined from their connections to other nodes (and
> the weight of those connections). Those connections are also called edges
> and carry a weight in a weighted graph. Available layout algorithms are:
> 
> * Kamada-Kawai uses the Kamada-Kawai path-length cost function. It is
>   not optimal for large networks because it is computationally intensive.
> * Spectral layout determines position using the eigenvectors of the graph
>   Laplacian.
> * Spring layout uses the Fruchterman-Reingold force-directed algorithm. It
>   is suitable for large networks with a high number of nodes and is the
>   default method.
> 
> For large graphs (~10 000 nodes), a spring layout run can take about 5
> minutes depending on hardware. Layout details are documented in the
> NetworkX drawing reference [2].
> 
> **Importing data**
> 
> If --import-data is set, descriptive data are stored as node attributes in
> the .gml file. This is useful for later visualization or statistical
> analysis (see
> [AverageWeightedPath][neurostatx.cli.AverageWeightedPath.AverageWeightedPath]
> or
> [PartialLeastSquareRegression][neurostatx.cli.PartialLeastSquareRegression.PartialLeastSquareRegression])
> and reduces the chance of subject mismatch.
> 
> The concept of this script was initially proposed in [1].

> **References**
>
> [1] [Ariza-Jiménez, L., Villa, L. F., & Quintero, O. L. (2019). Memberships
> Networks forHigh-Dimensional Fuzzy Clustering
> Visualization](https://doi.org/10.1007/978-3-030-31019-6_23)
> 
> [2] [NetworkX drawing
> documentation](https://networkx.org/documentation/stable/reference/drawing.html)

**Parameters**

- `in_dataset` (`str`) — Input dataset containing membership values for each cluster.
- `id_column` (`str`) — Name of the column containing the subject's ID tag. Required for proper handling of IDs.
- `desc_columns` (`int`) — Number of descriptive columns at the beginning of the dataset.
- `out_folder` (`str`) (default: `'./graph_results/'`) — Path of the folder in which the results will be written. If not specified, current folder and default name will be used. Defaults to ``./graph_results/``.
- `verbose` (`bool`) (default: `False`) — If true, produce verbose output. Defaults to False.
- `overwrite` (`bool`) (default: `False`) — If true, force overwriting of existing output files. Defaults to False.
- `save_parameters` (`bool`) (default: `False`) — If true, save the parameters used in a .txt file. Defaults to False.
- `plot_distribution` (`bool`) (default: `False`) — If true, will plot the membership distribution and delta. Defaults to False.
- `import_data` (`bool`) (default: `False`) — If true, will import the data from the input dataset within the graph network file. Defaults to False.
- `layout` (`NetworkLayout`) (default: `NetworkLayout.Spring`) — Layout algorithm to determine the nodes position. Defaults to Spring.
- `method` (`str`) (default: `'force'`) — Layout method passed to the NetworkX layout algorithm. Defaults to ``force``.
- `seed` (`int`) (default: `42`) — Random seed used when computing node positions. Defaults to 42.
- `weight` (`str`) (default: `'membership'`) — Name of the column containing the edge weight. Defaults to ``membership``.

**Examples**

```bash
ComputeGraphNetwork --in-dataset cluster_membership.xlsx
--id-column subjectkey --desc-columns 1 --out-folder output/
```

[View source](https://github.com/gagnonanthony/NeuroStatX/blob/main/neurostatx/cli/ComputeGraphNetwork.py#L24-L272)