### PageRank Initialization and Usage Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/ranking/pagerank.html

Demonstrates how to initialize the PageRank class and compute scores for a given graph. The example uses the 'house' dataset and specifies a personalized restart distribution.

```python
from sknetwork.ranking import PageRank
from sknetwork.data import house
pagerank = PageRank()
adjacency = house()
weights = {0: 1}
scores = pagerank.fit_predict(adjacency, weights)
np.round(scores, 2)
```

--------------------------------

### Load Dataset Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/data/load.html

Demonstrates how to save a dataset and then load it back using sknetwork.data.save and sknetwork.data.load.

```python
from sknetwork.data import save
from sknetwork.data import load
from scipy import sparse
import numpy as np
from pathlib import Path

# Assuming Dataset class is defined elsewhere and has adjacency and names attributes
# For demonstration, we'll mock a simple structure
class MockDataset:
    def __init__(self):
        self.adjacency = None
        self.names = None

dataset = MockDataset()
dataset.adjacency = sparse.csr_matrix(np.random.random((3, 3)) < 0.5)
dataset.names = np.array(['a', 'b', 'c'])
save('dataset', dataset)
dataset = load('dataset')
print(dataset.names)
```

--------------------------------

### PropagationClustering Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/clustering/propagation_clustering.html

Demonstrates how to use PropagationClustering to find clusters in a graph. The example uses the karate club dataset and fits the model to the adjacency matrix.

```python
from sknetwork.clustering import PropagationClustering
from sknetwork.data import karate_club
propagation = PropagationClustering()
graph = karate_club(metadata=True)
adjacency = graph.adjacency
labels = propagation.fit_predict(adjacency)
len(set(labels))
```

--------------------------------

### PageRank Calculation Example

Source: https://scikit-network.readthedocs.io/en/latest/reference/ranking.html

Demonstrates how to compute PageRank scores for nodes in a graph using the PageRank class. The example initializes PageRank, creates a graph, and then fits and predicts the scores.

```python
from sknetwork.ranking import PageRank
from sknetwork.data import house
pagerank = PageRank()
adjacency = house()
weights = {0: 1}
scores = pagerank.fit_predict(adjacency, weights)
np.round(scores, 2)

```

--------------------------------

### LouvainEmbedding Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/embedding/louvain_embedding.html

Demonstrates how to use LouvainEmbedding to get an embedding of a graph. The embedding's shape indicates the number of nodes and the number of detected communities.

```python
from sknetwork.embedding import LouvainEmbedding
from sknetwork.data import house

louvain = LouvainEmbedding()
adjacency = house()
embedding = louvain.fit_transform(adjacency)
print(embedding.shape)
```

--------------------------------

### Leiden Algorithm Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/clustering/leiden.html

Demonstrates how to use the Leiden algorithm to find clusters in a graph. Requires importing the Leiden class and a graph dataset.

```python
from sknetwork.clustering import Leiden
from sknetwork.data import karate_club

leiden = Leiden()
adjacency = karate_club()
labels = leiden.fit_predict(adjacency)
print(len(set(labels)))
```

--------------------------------

### LouvainHierarchy Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/hierarchy/louvain_hierarchy.html

Demonstrates how to use LouvainHierarchy for hierarchical clustering. It fits the model to the graph and predicts the clustering at each level.

```python
from sknetwork.hierarchy import LouvainHierarchy
from sknetwork.data import house
louvain = LouvainHierarchy()
adjacency = house()
louvain.fit_predict(adjacency)
```

--------------------------------

### Louvain Algorithm Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/clustering/louvain.html

Demonstrates how to use the Louvain algorithm to cluster nodes in a graph. The algorithm fits the graph and predicts the community labels for each node.

```python
from sknetwork.clustering import Louvain
from sknetwork.data import karate_club
louvain = Louvain()
adjacency = karate_club()
labels = louvain.fit_predict(adjacency)
len(set(labels))
```

--------------------------------

### Create a Mask for Positive Examples

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/recommendation.ipynb.txt

Generates a boolean mask to identify positive examples (movies watched by the user) for use in recommendation algorithms.

```python
mask = np.zeros(len(names), dtype=bool)
mask[targets] = 1
```

--------------------------------

### Import toy graph datasets and visualization functions

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/data/toy_graphs.ipynb.txt

Imports various toy graph datasets and the visualization functions from scikit-network. Ensure these are installed before running.

```python
from sknetwork.data import house, bow_tie, karate_club, miserables, painters, hourglass, star_wars, movie_actor
from sknetwork.visualization import visualize_graph, visualize_bigraph
```

--------------------------------

### HITS Algorithm Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/ranking/hits.html

Demonstrates how to use the HITS class to compute hub and authority scores on a biadjacency matrix. The example uses the star_wars dataset and prints the rounded scores.

```python
from sknetwork.ranking import HITS
from sknetwork.data import star_wars
hits = HITS()
biadjacency = star_wars()
scores = hits.fit_predict(biadjacency)
np.round(scores, 2)
```

--------------------------------

### Initialize DiffusionClassifier

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/classification/metrics.ipynb.txt

Initializes the DiffusionClassifier model. No specific setup is required before this step.

```python
diffusion = DiffusionClassifier()
```

--------------------------------

### HITS Algorithm Example

Source: https://scikit-network.readthedocs.io/en/latest/reference/ranking.html

Demonstrates how to use the HITS algorithm to compute hub and authority scores on a bipartite graph. Requires importing HITS and a graph dataset.

```python
from sknetwork.ranking import HITS
from sknetwork.data import star_wars
hits = HITS()
biadjacency = star_wars()
scores = hits.fit_predict(biadjacency)
import numpy as np
np.round(scores, 2)

```

--------------------------------

### NNLinker Initialization and Usage Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/linkpred/nn.html

Demonstrates how to initialize NNLinker and use it for link prediction on the karate club graph. The number of neighbors and the similarity threshold can be configured.

```python
from sknetwork.linkpred import NNLinker
from sknetwork.data import karate_club
linker = NNLinker(n_neighbors=5, threshold=0.5)
graph = karate_club(metadata=True)
adjacency = graph.adjacency
links = linker.fit_predict(adjacency)
print(links.shape)
```

--------------------------------

### Install scikit-network from source

Source: https://scikit-network.readthedocs.io/en/latest/first_steps.html

Install scikit-network by downloading the source code and running the setup script. This is useful for development or custom builds.

```bash
cd <scikit-network folder>
python setup.py develop

```

--------------------------------

### ForceAtlas Initialization and Fit Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/embedding/force_atlas.html

Demonstrates how to initialize and use the ForceAtlas layout algorithm to compute node positions for a graph. Requires the karate_club dataset and the ForceAtlas class.

```python
from sknetwork.embedding.force_atlas import ForceAtlas
from sknetwork.data import karate_club

force_atlas = ForceAtlas()
adjacency = karate_club()
embedding = force_atlas.fit_transform(adjacency)
print(embedding.shape)
```

--------------------------------

### Install scikit-network

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/overview/get_started.ipynb.txt

Use pip to install the scikit-network package. Uncomment the line to execute the installation.

```python
# uncomment to install
# !pip install scikit-network
```

--------------------------------

### SparseLR Class Initialization and Basic Usage

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/linalg/sparse_lowrank.html

Demonstrates how to initialize the SparseLR class with a sparse matrix and a low-rank component, and perform basic operations like dot product and sum.

```python
from scipy import sparse
from sknetwork.linalg import SparseLR
adjacency = sparse.eye(2, format='csr')
slr = SparseLR(adjacency, (np.ones(2), np.ones(2)))
x = np.ones(2)
slr.dot(x)
slr.sum(axis=0)
slr.sum(axis=1)
float(slr.sum())
```

--------------------------------

### Install scikit-network

Source: https://scikit-network.readthedocs.io/en/latest/index.html

Use pip to install the scikit-network library.

```bash
$ pip install scikit-network

```

--------------------------------

### Dirichlet Regression Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/regression/diffusion.html

Demonstrates how to use the Dirichlet class to predict node values based on initial conditions. Requires importing Dirichlet and a graph dataset.

```python
from sknetwork.regression import Dirichlet
from sknetwork.data import house
dirichlet = Dirichlet()
adjacency = house()
values = {0: 1, 2: 0}
values_pred = dirichlet.fit_predict(adjacency, values)
np.round(values_pred, 2)
```

--------------------------------

### Spring Layout Fit Method with Custom Initialization

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/embedding/spring.html

Shows how to use the `fit` method of the Spring layout with custom initial positions. It also demonstrates overriding the default number of iterations.

```python
from sknetwork.embedding import Spring
from sknetwork.data import karate_club

spring = Spring(n_iter=100)
adjacency = karate_club()
position_init = np.random.randn(adjacency.shape[0], 2)
spring.fit(adjacency, position_init=position_init, n_iter=50)
print(spring.embedding_.shape)
```

--------------------------------

### Install Scikit-network

Source: https://scikit-network.readthedocs.io/en/latest/_sources/first_steps.rst.txt

Install scikit-network using pip. This is the recommended method for most users.

```console
pip install scikit-network
```

--------------------------------

### Install scikit-network via pip

Source: https://scikit-network.readthedocs.io/en/latest/first_steps.html

Use this command to install the package directly from the Python Package Index.

```bash
pip install scikit-network

```

--------------------------------

### Install Scikit-network from Source

Source: https://scikit-network.readthedocs.io/en/latest/_sources/first_steps.rst.txt

Install scikit-network from source code. This method is useful for developers or when specific versions are needed.

```console
cd <scikit-network folder>
python setup.py develop
```

--------------------------------

### Initialize and Use LouvainEmbedding

Source: https://scikit-network.readthedocs.io/en/latest/reference/embedding.html

Demonstrates how to initialize the LouvainEmbedding class and use it to compute the embedding of a graph. The adjacency matrix is obtained from the 'house' dataset.

```python
>>> from sknetwork.embedding import LouvainEmbedding
>>> from sknetwork.data import house
>>> louvain = LouvainEmbedding()
>>> adjacency = house()
>>> embedding = louvain.fit_transform(adjacency)
>>> embedding.shape
(5, 2)
```

--------------------------------

### Get Node Index for Dendrogram

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/visualization/dendrograms.html

Helper function to get the order of nodes in a dendrogram, with an option to reorder based on subtree size.

```python
def get_index(dendrogram, reorder=True):
    """Index nodes for pretty dendrogram."""
    n = dendrogram.shape[0] + 1
    tree = {i: [i] for i in range(n)}
    for t in range(n - 1):
        i = int(dendrogram[t, 0])
        j = int(dendrogram[t, 1])
        left: list = tree.pop(i)
        right: list = tree.pop(j)
        if reorder and len(left) < len(right):
            tree[n + t] = right + left
        else:
            tree[n + t] = left + right
    return list(tree.values())[0]
```

--------------------------------

### Initialize Louvain Clustering

Source: https://scikit-network.readthedocs.io/en/latest/use_cases/votes.html

Instantiate the Louvain clustering algorithm object.

```python
louvain = Louvain()
```

--------------------------------

### K-Centers Initialization and Iteration

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/clustering/kcenters.html

This snippet illustrates the core logic of the K-Centers algorithm, including node assignment to centers using PageRank and the iterative refinement of center selection. It handles both bipartite and non-bipartite graphs and selects the best clustering based on modularity across multiple initializations.

```python
if min(ppr_scores) == 0:
                center = np.random.choice(nodes[mask][ppr_scores == 0])
            else:
                probs = 1 / ppr_scores
                probs = probs / np.sum(probs)
                center = np.random.choice(nodes[mask], p=probs)

            mask[center] = 0
            centers.append(center)
            weights.update({center: 1})

        centers = np.array(centers)
        return centers
```

```python
if self.n_clusters < 2:
            raise ValueError("The number of clusters must be at least 2.")

        if self.n_init < 1:
            raise ValueError("The n_init parameter must be at least 1.")

        if self.directed:
            input_matrix = directed2undirected(input_matrix)

        adjacency, self.bipartite = get_adjacency(input_matrix, force_bipartite=force_bipartite)
        n_row = input_matrix.shape[0]
        n_nodes = adjacency.shape[0]
        nodes = np.arange(n_nodes)

        mask = self._compute_mask_centers(input_matrix)
        if self.n_clusters > np.sum(mask):
            raise ValueError("The number of clusters is to high. This might be due to the center_position parameter.")

        pagerank_clf = PageRankClassifier()
        pagerank = PageRank()

        labels_ = []
        centers_ = []
        modularity_ = []

        # Restarts
        for i in range(self.n_init):

            # Initialization
            centers = self._init_centers(adjacency, mask, self.n_clusters)
            prev_centers = None
            labels = None
            n_iter = 0

            while not np.equal(prev_centers, centers).all() and (n_iter < self.max_iter):

                # Assign nodes to centers
                labels_center = {center: label for label, center in enumerate(centers)}
                labels = pagerank_clf.fit_predict(adjacency, labels_center)

                # Find new centers
                prev_centers = centers.copy()
                new_centers = []

                for label in np.unique(labels):
                    mask_cluster = labels == label
                    mask_cluster &= mask
                    scores = pagerank.fit_predict(adjacency, weights=mask_cluster)
                    scores[~mask_cluster] = 0
                    new_centers.append(nodes[np.argmax(scores)])

                n_iter += 1

            # Store results
            if self.bipartite:
                labels_row = labels[:n_row]
                labels_col = labels[n_row:]
                modularity = get_modularity(input_matrix, labels_row, labels_col)
            else:
                modularity = get_modularity(adjacency, labels)

            labels_.append(labels)
            centers_.append(centers)
            modularity_.append(modularity)

        # Select restart with the highest modularity
        idx_max = np.argmax(modularity_)
        self.labels_ = np.array(labels_[idx_max])
        self.centers_ = np.array(centers_[idx_max])

        if self.bipartite:
            self._split_vars(input_matrix.shape)

            # Define centers based on center position
            if self.center_position == "row":
                self.centers_row_ = self.centers_
            elif self.center_position == "col":
                self.centers_col_ = self.centers_ - n_row
            else:
                self.centers_row_ = self.centers_[self.centers_ < n_row]
                self.centers_col_ = self.centers_[~np.isin(self.centers_, self.centers_row_)] - n_row

        return self
```

--------------------------------

### Importing necessary libraries

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/clustering/kcenters.ipynb.txt

Before running the k-centers algorithm, import the required libraries: `numpy` for numerical operations and `scikit-network` for the clustering algorithm.

```python
import numpy as np
from sknetwork.clustering import k_centers
```

--------------------------------

### KCenters Clustering Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/clustering/kcenters.html

Demonstrates how to use the KCenters class to perform clustering on a graph. Requires the karate club dataset and the KCenters class. The output shows the number of clusters found.

```python
from sknetwork.clustering import KCenters
from sknetwork.data import karate_club
kcenters = KCenters(n_clusters=2)
adjacency = karate_club()
labels = kcenters.fit_predict(adjacency)
len(set(labels))
```

--------------------------------

### Spring Layout Initialization and Transformation

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/embedding/spring.html

Demonstrates how to initialize the Spring layout and apply it to a graph. The `fit_transform` method computes the node embeddings. The resulting embedding shape is printed.

```python
from sknetwork.embedding import Spring
from sknetwork.data import karate_club

spring = Spring()
adjacency = karate_club()
embedding = spring.fit_transform(adjacency)
print(embedding.shape)
```

--------------------------------

### Get Neighbors of a Node in a Graph

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/utils/neighbors.html

Retrieves the neighbors (successors by default) of a specific node in a graph. For directed graphs, set `transpose=True` to get predecessors. Works with adjacency or biadjacency matrices.

```python
from sknetwork.data import house
adjacency = house()
print(get_neighbors(adjacency, node=0))
```

--------------------------------

### Get Node Neighbors

Source: https://scikit-network.readthedocs.io/en/latest/reference/utils.html

Finds the direct neighbors of a specified node. For directed graphs, this returns successors by default; set transpose=True to get predecessors. This function is applicable to both adjacency and biadjacency matrices.

```python
from sknetwork.data import house
adjacency = house()
get_neighbors(adjacency, node=0)
array([1, 4], dtype=int32)
```

--------------------------------

### Initialize and Use Propagation for Node Classification

Source: https://scikit-network.readthedocs.io/en/latest/reference/classification.html

This example demonstrates how to initialize the Propagation class and use it to predict labels on a graph. It requires importing the class and a dataset, then fitting the model with initial labels and predicting the rest.

```python
>>> from sknetwork.classification import Propagation
>>> from sknetwork.data import karate_club
>>> propagation = Propagation()
>>> graph = karate_club(metadata=True)
>>> adjacency = graph.adjacency
>>> labels_true = graph.labels
>>> labels = {0: labels_true[0], 33: labels_true[33]}
>>> labels_pred = propagation.fit_predict(adjacency, labels)
>>> float(np.round(np.mean(labels_pred == labels_true), 2))
0.94
```

--------------------------------

### Get Weights of Nodes in a Graph

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/utils/neighbors.html

Computes the total weight of outgoing links for each node (out-weights). If the graph is unweighted, it returns the out-degrees. Set `transpose=True` to get in-weights. Works for weighted and unweighted graphs.

```python
from sknetwork.data import house
adjacency = house()
print(get_weights(adjacency))
```

--------------------------------

### GNNClassifier Example: Karate Club Node Classification

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/gnn/gnn_classifier.html

Demonstrates how to use GNNClassifier for node classification on the Karate Club dataset. It shows model initialization, fitting, and prediction, with early stopping disabled for this example.

```python
from sknetwork.gnn.gnn_classifier import GNNClassifier
from sknetwork.data import karate_club
from numpy.random import randint
graph = karate_club(metadata=True)
adjacency = graph.adjacency
labels_true = graph.labels
labels = {i: labels_true[i] for i in [0, 1, 33]}
features = adjacency.copy()
gnn = GNNClassifier(dims=1, early_stopping=False)
labels_pred = gnn.fit_predict(adjacency, features, labels, random_state=42)
float(round(np.mean(labels_pred == labels_true), 2))
```

--------------------------------

### Initialize and Apply Louvain Clustering

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/votes.ipynb.txt

Initializes the Louvain clustering algorithm and applies it to the 'for' vote biadjacency matrix to predict community labels.

```python
louvain = Louvain()
```

```python
labels_pred = louvain.fit_predict(biadjacency_for)
```

```python
np.unique(labels_pred, return_counts=True)
```

--------------------------------

### NNLinker.get_params

Source: https://scikit-network.readthedocs.io/en/latest/reference/linkpred.html

Get parameters as dictionary.

```APIDOC
## get_params

### Description
Get parameters as dictionary.

### Returns
#### Returns
- **params** (dict) - Parameters of the algorithm.

### Return type
dict
```

--------------------------------

### Louvain Initialization

Source: https://scikit-network.readthedocs.io/en/latest/reference/hierarchy.html

Initializes the Louvain algorithm with an input matrix and an optional flag to force bipartite interpretation.

```APIDOC
## Louvain

### Parameters

* **input_matrix** (_sparse.csr_matrix_ _,__np.ndarray_) – Adjacency matrix or biadjacency matrix of the graph.
* **force_bipartite** – If `True`, force the input matrix to be considered as a biadjacency matrix.

### Returns

**self** 

### Return type

`LouvainIteration`
```

--------------------------------

### sknetwork.path.breadth_first_search

Source: https://scikit-network.readthedocs.io/en/latest/reference/path.html

Breadth-first ordering starting from some node.

```APIDOC
## sknetwork.path.breadth_first_search

### Description
Breadth-first ordering starting from some node.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **adjacency** (csr_matrix) - Adjacency matrix of the graph.
* **source** (int) - Source node.

### Returns
**index** – Node index corresponding to the breadth-first-search from the source. The length of the vector is the number of nodes reachable from the source.

Return type:
np.ndarray
```

--------------------------------

### Initialize and Fit Force Atlas Layout

Source: https://scikit-network.readthedocs.io/en/latest/reference/embedding.html

Demonstrates how to initialize the ForceAtlas class and compute the node embeddings using the fit_transform method with the karate club dataset. The shape of the resulting embedding is then checked.

```python
>>> from sknetwork.embedding.force_atlas import ForceAtlas
>>> from sknetwork.data import karate_club
>>> force_atlas = ForceAtlas()
>>> adjacency = karate_club()
>>> embedding = force_atlas.fit_transform(adjacency)
>>> embedding.shape
(34, 2)
```

--------------------------------

### sknetwork.hierarchy.reorder_dendrogram

Source: https://scikit-network.readthedocs.io/en/latest/_sources/reference/hierarchy.rst.txt

Reorders the nodes in a dendrogram, for example, for visualization purposes.

```APIDOC
## sknetwork.hierarchy.reorder_dendrogram

### Description
Reorders the nodes in a dendrogram, for example, for visualization purposes.

### Function
`sknetwork.hierarchy.reorder_dendrogram`
```

--------------------------------

### reindex_labels

Source: https://scikit-network.readthedocs.io/en/latest/_sources/reference/clustering.rst.txt

Reindexes the cluster labels to be contiguous starting from 0.

```APIDOC
## reindex_labels

### Description
Reindexes the cluster labels to be contiguous starting from 0.

### Parameters
- **labels** (ndarray): The cluster labels to reindex.
```

--------------------------------

### sknetwork.path.get_distances

Source: https://scikit-network.readthedocs.io/en/latest/reference/path.html

Get the distances from a source (or a set of sources) in number of hops.

```APIDOC
## sknetwork.path.get_distances

### Description
Get the distances from a source (or a set of sources) in number of hops.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **input_matrix** (csr_matrix) - Adjacency matrix or biadjacency matrix of the graph.
* **source** (int | Iterable | None) - If an integer, index of the source node. If a list or array, indices of source nodes (the shortest distances to one of these nodes is returned).
* **source_row** (int | Iterable | None) - For bipartite graphs, index of source nodes on rows and columns. The parameter source_row is an alias for source (at least one of them must be `None`).
* **source_col** (int | Iterable | None) - For bipartite graphs, index of source nodes on rows and columns. The parameter source_row is an alias for source (at least one of them must be `None`).
* **transpose** (bool) - If `True`, transpose the input matrix.
* **force_bipartite** (bool) - If `True`, consider the input matrix as the biadjacency matrix of a bipartite graph. Set to `True` is the parameters source_row or source_col re specified.

### Returns
**distances** – Vector of distances from source (distance = -1 if no path exists from the source). For a bipartite graph, two vectors are returned, one for the rows and one for the columns.

Return type:
np.ndarray of shape (n_nodes,)

### Request Example
```python
>>> from sknetwork.data import cyclic_digraph
>>> adjacency = cyclic_digraph(3)
>>> get_distances(adjacency, source=0)
array([0, 1, 2])
>>> get_distances(adjacency, source=[0, 2])
array([0, 1, 0])
```
```

--------------------------------

### Import necessary libraries

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/visualization/pie_charts.ipynb.txt

Imports IPython.display for SVG output and scipy.sparse for sparse matrix operations.

```python
from IPython.display import SVG
from scipy import sparse
```

--------------------------------

### Initialize Louvain Algorithm

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/wikipedia.ipynb.txt

Initializes the Louvain algorithm for graph clustering. This is the first step before applying the clustering to a graph.

```python
algo = Louvain()
```

--------------------------------

### sknetwork.path.get_shortest_path

Source: https://scikit-network.readthedocs.io/en/latest/reference/path.html

Get the shortest paths from a source (or a set of sources) in number of hops.

```APIDOC
## sknetwork.path.get_shortest_path

### Description
Get the shortest paths from a source (or a set of sources) in number of hops.

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **input_matrix** (csr_matrix) - Adjacency matrix or biadjacency matrix of the graph.
* **source** (int | Iterable | None) - If an integer, index of the source node. If a list, indices of source nodes (the shortest distances to one of these nodes in returned).
* **source_row** (int | Iterable | None) - For bipartite graphs, index of source nodes on rows and columns. The parameter source_row is an alias for source (at least one of them must be `None`).
* **source_col** (int | Iterable | None) - For bipartite graphs, index of source nodes on rows and columns. The parameter source_row is an alias for source (at least one of them must be `None`).
* **force_bipartite** (bool) - If `True`, consider the input matrix as the biadjacency matrix of a bipartite graph. Set to `True` is the parameters source_row or source_col are specified.

### Returns
**path** – Adjacency matrix of the graph of the shortest paths from the source node (or the set of source nodes). If the input graph is a bipartite graph, the shape of the matrix is (n_row + n_col, n_row + n_col) with the new index corresponding to the rows then the columns of the original graph.

Return type:
sparse.csr_matrix

### Request Example
```python
>>> from sknetwork.data import cyclic_digraph
>>> adjacency = cyclic_digraph(3)
>>> path = get_shortest_path(adjacency, source=0)
>>> path.toarray().astype(int)
array([[0, 1, 0],
       [0, 0, 1],
       [0, 0, 0]])
```
```

--------------------------------

### Print Beginning of Text

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/text.ipynb.txt

Prints the first 494 characters of the loaded text to the console.

```python
print(text[:494])
```

--------------------------------

### Get 'People' Category ID

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/wikipedia.ipynb.txt

Retrieves the numerical ID for the 'People' category from a mapping.

```python
people = label_id['People']
```

--------------------------------

### NNLinker Example: Predicting Links

Source: https://scikit-network.readthedocs.io/en/latest/reference/linkpred.html

Demonstrates how to initialize NNLinker, fit it to a graph's adjacency matrix, and predict links. The resulting links matrix shape is shown.

```python
>>> from sknetwork.linkpred import NNLinker
>>> from sknetwork.data import karate_club
>>> linker = NNLinker(n_neighbors=5, threshold=0.5)
>>> graph = karate_club(metadata=True)
>>> adjacency = graph.adjacency
>>> links = linker.fit_predict(adjacency)
>>> links.shape
(34, 34)
```

--------------------------------

### Prepare Training Data

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/gnn/gnn_classifier.ipynb.txt

Sets up the training labels by masking a portion of the true labels.

```python
# Training set
labels = labels_true.copy()
np.random.seed(42)
train_mask = np.random.random(size=len(labels)) < 0.5
labels[train_mask] = -1
```

--------------------------------

### Get Length of Text

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/text.ipynb.txt

Calculates and returns the total number of characters in the loaded text.

```python
len(text)
```

--------------------------------

### Get Core Decomposition Values

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/sport.ipynb.txt

Calculates the core decomposition values for an undirected graph.

```python
values = get_core_decomposition(adjacency_sym)
```

--------------------------------

### Import Visualization Tools

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/overview/get_started.ipynb.txt

Import necessary functions for graph visualization, including SVG display and the visualize_graph function.

```python
from IPython.display import SVG
from sknetwork.visualization import visualize_graph
```

--------------------------------

### Import necessary libraries

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/clustering/louvain.html

Imports all required modules for data loading, clustering, normalization, and visualization.

```python
from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.clustering import Louvain, get_modularity
from sknetwork.linalg import normalize
from sknetwork.utils import get_membership
from sknetwork.visualization import visualize_graph, visualize_bigraph
```

--------------------------------

### Get dimensions of the biadjacency matrix

Source: https://scikit-network.readthedocs.io/en/latest/use_cases/votes.html

Determines the number of deputies and bills from the shape of the biadjacency matrix.

```python
n_deputy, n_bill = biadjacency.shape
```

--------------------------------

### Import visualization functions and data loaders

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/visualization/graphs.ipynb.txt

Import data loading functions like `karate_club` and visualization functions `visualize_graph`, `visualize_bigraph`.

```python
from sknetwork.data import karate_club, painters, movie_actor, load_netset
from sknetwork.visualization import visualize_graph, visualize_bigraph
```

--------------------------------

### Get Number of Movies and Users

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/recommendation.ipynb.txt

Determines the number of movies and users from the shape of the biadjacency matrix.

```python
n_movies, n_users = biadjacency.shape
```

--------------------------------

### Import necessary libraries

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/embedding/gsvd.html

Import libraries for data handling, GSVD, and visualization.

```python
from IPython.display import SVG
```

```python
import numpy as np
```

```python
from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.embedding import GSVD
from sknetwork.visualization import visualize_graph, visualize_bigraph
```

--------------------------------

### Get Bias Shapes in GraphSAGE

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/gnn/gnn_classifier.ipynb.txt

Displays the shapes of the bias vectors for each layer in the GraphSAGE model.

```python
[bias.shape for bias in biases]

```

--------------------------------

### Import necessary libraries

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/clustering/propagation.html

Imports modules for data loading, clustering, linear algebra, utility functions, and visualization.

```python
from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.clustering import PropagationClustering, get_modularity
from sknetwork.linalg import normalize
from sknetwork.utils import get_membership
from sknetwork.visualization import visualize_graph, visualize_bigraph
```

--------------------------------

### Get Weight Shapes in GraphSAGE

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/gnn/gnn_classifier.ipynb.txt

Displays the shapes of the weight matrices for each layer in the GraphSAGE model.

```python
[weight.shape for weight in weights]
```

--------------------------------

### Import necessary libraries

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/classification/propagation.ipynb.txt

Imports `SVG` for displaying visualizations.

```python
from IPython.display import SVG
```

--------------------------------

### Set up local development environment

Source: https://scikit-network.readthedocs.io/en/latest/contributing.html

Install scikit-network into a virtual environment for local development using virtualenvwrapper.

```bash
$ mkvirtualenv sknetwork
$ cd sknetwork/
$ python setup.py develop

```

--------------------------------

### Label Propagation Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/classification/propagation.html

Demonstrates how to use the Propagation classifier to predict node labels on a graph. Initializes the classifier, fits it to the adjacency matrix and known labels, and predicts the labels for the remaining nodes.

```python
from sknetwork.classification import Propagation
from sknetwork.data import karate_club
propagation = Propagation()
graph = karate_club(metadata=True)
adjacency = graph.adjacency
labels_true = graph.labels
labels = {0: labels_true[0], 33: labels_true[33]}
labels_pred = propagation.fit_predict(adjacency, labels)
float(np.round(np.mean(labels_pred == labels_true), 2))
```

--------------------------------

### Get Prediction Probabilities

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/gnn/gnn_classifier.html

Obtains the probability distribution over labels for each node in the graph from the trained GNN classifier.

```python
# probability distribution over labels
probs = gnn.predict_proba()

```

--------------------------------

### Get Length of Main Text

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/text.ipynb.txt

Calculates and returns the number of characters in the extracted main text content.

```python
len(main)
```

--------------------------------

### Configure and fit RandomProjection for bipartite graphs

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/embedding/random_projection.html

Initializes RandomProjection with specific parameters (normalized=False) and fits it to a bipartite graph's biadjacency matrix. The default parameters are shown for reference.

```python
graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col
```

```python
projection = RandomProjection(2, normalized=False)
projection.fit(biadjacency)
```

```python
RandomProjection(n_components=2, alpha=0.5, n_iter=3, random_walk=False, regularization=-1, normalized=False)
```

--------------------------------

### Get probability distribution over labels

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/classification/diffusion.ipynb.txt

Calculates the probability distribution for each node belonging to each class after fitting the classifier.

```python
# probability distribution over labels
probs = diffusion.predict_proba()
```

--------------------------------

### Import visualization and clustering tools

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/visualization/pie_charts.ipynb.txt

Imports functions for graph data generation, visualization, and Louvain clustering.

```python
from sknetwork.data import bow_tie, karate_club, painters
from sknetwork.visualization import visualize_graph
from sknetwork.clustering import Louvain
```

--------------------------------

### Prepare Training Data

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/gnn/gnn_classifier.html

Prepares the training labels by copying the true labels and masking a portion for training. Uses a random seed for reproducibility.

```python
# Training set
labels = labels_true.copy()
np.random.seed(42)
train_mask = np.random.random(size=len(labels)) < 0.5
labels[train_mask] = -1

```

--------------------------------

### Get GNN Bias Shapes

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/gnn/gnn_classifier.html

Display the shapes of the biases for each layer in the GNN. This shows the dimensionality of the learned biases.

```python
[bias.shape for bias in biases]
```

--------------------------------

### Count Column Names

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/data/load_data.html

Get the total number of column names, which represents the number of features in the binarized dataset.

```python
len(names_col)
```

--------------------------------

### Import Bunch, load, and save functions

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/data/save.ipynb.txt

Import the core components for data handling: Bunch for structuring graph data, and load/save for persistence.

```python
from sknetwork.data import Bunch, load, save
```

--------------------------------

### Perform Louvain embedding

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/embedding/louvain_embedding.ipynb.txt

Applies the LouvainEmbedding algorithm to the graph's adjacency matrix and gets the embedding shape.

```python
louvain = LouvainEmbedding()
embedding = louvain.fit_transform(adjacency)
embedding.shape
```

--------------------------------

### Import classification and visualization tools

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/classification/propagation.ipynb.txt

Imports graph datasets, the `Propagation` classifier, and visualization functions.

```python
from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.classification import Propagation
from sknetwork.visualization import svg_graph, visualize_bigraph
```

--------------------------------

### sknetwork.data.bow_tie

Source: https://scikit-network.readthedocs.io/en/latest/_sources/reference/data.rst.txt

Generates a toy graph representing a bow-tie structure. This is a predefined graph useful for network analysis examples.

```APIDOC
## sknetwork.data.bow_tie

### Description
Generates a toy graph representing a bow-tie structure.

### Method
Not applicable (Python function)

### Parameters
(Details not provided in source)

### Request Example
(Not provided in source)

### Response
(Details not provided in source)
```

--------------------------------

### Instantiate Optimizer with get_optimizer

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/gnn/optimizer.html

Use the get_optimizer function to create an optimizer instance. It supports 'Adam' and 'GD' (Gradient Descent) by name, or accepts a custom optimizer object. Specify the learning rate when using named optimizers.

```python
from typing import Union
from sknetwork.gnn.optimizer import get_optimizer, BaseOptimizer, ADAM, GD

# Example usage:
optimizer_adam = get_optimizer('Adam', learning_rate=0.005)
print(f"Created optimizer: {type(optimizer_adam).__name__}")

optimizer_gd = get_optimizer('GD', learning_rate=0.01)
print(f"Created optimizer: {type(optimizer_gd).__name__}")

# Using a custom optimizer object
custom_optimizer = ADAM(learning_rate=0.001)
optimizer_custom = get_optimizer(custom_optimizer)
print(f"Created optimizer: {type(optimizer_custom).__name__}")

# Example of invalid input
try:
    get_optimizer('SGD')
except ValueError as e:
    print(f"Error: {e}")

try:
    get_optimizer(123)
except TypeError as e:
    print(f"Error: {e}")
```

--------------------------------

### Display Sample of Seen Movies

Source: https://scikit-network.readthedocs.io/en/latest/use_cases/recommendation.html

Shows a sample of the movie titles that the user has seen. This helps in understanding the input data for the recommendation process.

```python
# seen movies (sample)
names[targets][:10]
```

--------------------------------

### Calculate Personalized PageRank

Source: https://scikit-network.readthedocs.io/en/latest/use_cases/recommendation.html

Computes personalized PageRank scores, starting from a specific target movie (index 175).

```python
scores_ppr = pagerank.fit_predict(positive, weights={175:1})
```

--------------------------------

### Import scikit-network modules

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/visualization/dendrograms.html

Import data loading functions, the Paris clustering algorithm, and visualization utilities.

```python
from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.hierarchy import Paris
from sknetwork.visualization import visualize_graph, visualize_bigraph
from sknetwork.visualization import visualize_dendrogram
```

--------------------------------

### Visualize Louvain Embedding with Cytoscape

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/embedding/louvain_embedding.ipynb.txt

Visualize the computed Louvain embedding using a Cytoscape graph. This requires the 'cytoscape-jupyter-widget' to be installed.

```python
from sknetwork.visualization import svg_graph, display_graph

# Visualize the graph with Louvain communities
# The embedding object contains the community labels
image = svg_graph(G, embedding.labels_)
display_graph(image)
```

--------------------------------

### Initialize PageRank for Bipartite Graphs

Source: https://scikit-network.readthedocs.io/en/latest/_sources/tutorials/ranking/pagerank.ipynb.txt

Initializes the PageRank algorithm. This object will be used to compute scores on the bipartite graph.

```python
pagerank = PageRank()
```

--------------------------------

### Get cluster probabilities for bipartite graph

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/clustering/louvain.html

Calculates the probability distribution over clusters for both row and column nodes in the bipartite graph.

```python
# probability distribution over clusters
probs_row = louvain.predict_proba()
probs_col = louvain.predict_proba(columns=True)
```

--------------------------------

### Get cluster probabilities for Karate Club graph

Source: https://scikit-network.readthedocs.io/en/latest/tutorials/clustering/propagation.html

Calculates the probability distribution over clusters for each node in the Karate Club graph.

```python
probs = propagation.predict_proba()

# scores for cluster 1
scores = probs[:,1]

image = visualize_graph(adjacency, position, scores=scores)
SVG(image)
```

--------------------------------

### Cluster Karate Club Graph with Louvain Algorithm

Source: https://scikit-network.readthedocs.io/en/latest/_sources/first_steps.rst.txt

Example of clustering the Karate club graph using the Louvain algorithm. This demonstrates a common use case for graph analysis.

```python
from sknetwork.data import karate_club
from sknetwork.clustering import Louvain

adjacency = karate_club()
algorithm = Louvain()
algorithm.fit(adjacency)
```

--------------------------------

### Spring Class Initialization

Source: https://scikit-network.readthedocs.io/en/latest/reference/embedding.html

Initializes the Spring layout algorithm with specified parameters for graph embedding.

```APIDOC
## Spring Class

### Description
Initializes the Spring layout algorithm for displaying small graphs using a force-directed placement approach.

### Parameters
* **n_components** (int) - Optional - Dimension of the graph layout. Defaults to 2.
* **strength** (float) - Optional - Intensity of the force that moves the nodes. Defaults to None.
* **n_iter** (int) - Optional - Number of iterations to update positions. Defaults to 50.
* **tol** (float) - Optional - Minimum relative change in positions to continue updating. Defaults to 0.0001.
* **approx_radius** (float) - Optional - If a positive value is provided, only the nodes within this distance a given node are used to compute the repulsive force. Defaults to -1.
* **position_init** (str) - Optional - How to initialize the layout. If ‘spectral’, use Spectral embedding in dimension 2, otherwise, use random initialization. Defaults to 'random'.
```

--------------------------------

### Get k-core decomposition of a graph

Source: https://scikit-network.readthedocs.io/en/latest/reference/topology.html

Calculates the core value for each node in a graph. Use this to understand the hierarchical structure of a network.

```python
from sknetwork.data import karate_club
adjacency = karate_club()
core_values = get_core_decomposition(adjacency)
len(core_values)
```

--------------------------------

### Get Article Neighbors

Source: https://scikit-network.readthedocs.io/en/latest/_sources/use_cases/wikipedia.ipynb.txt

Retrieves the indices of neighboring articles for a given article and prints the names of the first 10 neighbors.

```python
neighbors = get_neighbors(adjacency, i)
print(names[neighbors[:10]])
```

--------------------------------

### Diffusion Example

Source: https://scikit-network.readthedocs.io/en/latest/_modules/sknetwork/regression/diffusion.html

Demonstrates how to use the Diffusion class to predict node values based on initial seed values and an adjacency matrix. The diffusion process is iterated a specified number of times with a given damping factor.

```python
from sknetwork.data import house
from sknetwork.regression import Diffusion

diffusion = Diffusion(n_iter=1)
adjacency = house()
values = {0: 1, 2: 0}
values_pred = diffusion.fit_predict(adjacency, values)
print(np.round(values_pred, 1))
```