### Install StellarGraph from Source with Demo Dependencies

Source: https://stellargraph.readthedocs.io/en/stable/README.html

Install StellarGraph from the local source code along with additional dependencies required for the demos. This is useful when working with the source code and wanting to run examples.

```bash
pip install .[demos]
```

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

### Install StellarGraph from Source

Source: https://stellargraph.readthedocs.io/en/stable/README.html

After cloning the repository, navigate to the directory and install StellarGraph using pip. This command installs the library from the local source code.

```bash
cd stellargraph
pip install .
```

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

### Install StellarGraph Demos with Pip

Source: https://stellargraph.readthedocs.io/en/stable/demos/index.html

Install the necessary dependencies to run most StellarGraph demo notebooks locally using pip.

```bash
pip install stellargraph[demos]
```

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

### Training Output Example

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/keras-node2vec-embeddings.ipynb

Example output showing the training progress, including steps per epoch, loss, and binary accuracy for each epoch.

```text
Train for 39760 steps
Epoch 1/2
39760/39760 [==============================] - 155s 4ms/step - loss: 0.2924 - binary_accuracy: 0.8557
Epoch 2/2
39760/39760 [==============================] - 238s 6ms/step - loss: 0.1096 - binary_accuracy: 0.9641
```

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

### Create Labeled Link Examples

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/ctdne-link-prediction.html

Combines positive and negative link examples into a single dataset with corresponding binary labels.

```python
def labelled_links(positive_examples, negative_examples):
    return (
        positive_examples + negative_examples,
        np.repeat([1, 0], [len(positive_examples), len(negative_examples)]),
    )


link_examples, link_labels = labelled_links(pos, neg)
link_examples_test, link_labels_test = labelled_links(pos_test, neg_test)
```

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

### Install StellarGraph using PyPI

Source: https://stellargraph.readthedocs.io/en/stable/README.html

Use this command to install the core StellarGraph library from the Python Package Index.

```bash
pip install stellargraph
```

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

### Install StellarGraph Demos with Conda

Source: https://stellargraph.readthedocs.io/en/stable/demos/index.html

Install the necessary dependencies to run most StellarGraph demo notebooks locally using conda.

```bash
conda install -c stellargraph stellargraph
```

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

### Generate and Verify Link Examples

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/ctdne-link-prediction.html

This code generates training and testing link examples using the previously defined functions and then prints the graph information along with the counts of positive and negative links for both sets. It's useful for verifying the data generation process.

```python
pos, neg = positive_and_negative_links(graph, edges_train)
pos_test, neg_test = positive_and_negative_links(graph, edges_test)

print(
    f"{graph.info()}\n"
    f"Training examples: {len(pos)} positive links, {len(neg)} negative links\n"
    f"Test examples: {len(pos_test)} positive links, {len(neg_test)} negative links"
)
```

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

### Attri2Vec Training Output Example

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/attri2vec-link-prediction.html

Example output showing the loss and binary accuracy for each epoch during Attri2Vec model training.

```text
Epoch 1/6
4711/4711 - 194s - loss: 0.7453 - binary_accuracy: 0.5219
Epoch 2/6
4711/4711 - 194s - loss: 0.6641 - binary_accuracy: 0.5677
Epoch 3/6
4711/4711 - 201s - loss: 0.5845 - binary_accuracy: 0.6375
Epoch 4/6
4711/4711 - 195s - loss: 0.5185 - binary_accuracy: 0.7198
Epoch 5/6
4711/4711 - 194s - loss: 0.4662 - binary_accuracy: 0.7767
Epoch 6/6
4711/4711 - 194s - loss: 0.4220 - binary_accuracy: 0.8110
```

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

### Setup Callbacks for Early Stopping and Checkpointing

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gat-node-classification.ipynb

Configure callbacks for early stopping based on validation accuracy and for saving the best model weights. Ensure the 'logs' directory exists.

```python
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

if not os.path.isdir("logs"):
    os.makedirs("logs")
es_callback = EarlyStopping(
    monitor="val_acc", patience=20
)  # patience is the number of epochs to wait before early stopping in case of no further improvement
mc_callback = ModelCheckpoint(
    "logs/best_model.h5", monitor="val_acc", save_best_only=True, save_weights_only=True
)
```

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

### Install StellarGraph with igraph for Community Detection

Source: https://stellargraph.readthedocs.io/en/stable/README.html

Install StellarGraph, demo dependencies, and the python-igraph library, which is necessary for community detection demos and may have platform-specific availability.

```bash
pip install stellargraph[demos,igraph]
```

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

### APPNP Training Output Logs

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/ppnp-node-classification.ipynb

Example output showing the training progress and validation metrics over multiple epochs.

```text
Using GCN (local pooling) filters...
  ['...']
  ['...']
Train for 1 steps, validate for 1 steps
Epoch 1/120
1/1 - 1s - loss: 2.1611 - acc: 0.1571 - val_loss: 2.0960 - val_acc: 0.3500
Epoch 2/120
1/1 - 0s - loss: 2.0830 - acc: 0.3214 - val_loss: 2.0560 - val_acc: 0.3780
Epoch 3/120
1/1 - 0s - loss: 2.0771 - acc: 0.2214 - val_loss: 2.0171 - val_acc: 0.3200
Epoch 4/120
1/1 - 0s - loss: 2.0152 - acc: 0.3286 - val_loss: 1.9767 - val_acc: 0.3040
Epoch 5/120
1/1 - 0s - loss: 1.9555 - acc: 0.3143 - val_loss: 1.9280 - val_acc: 0.3040
Epoch 6/120
1/1 - 0s - loss: 1.9276 - acc: 0.3357 - val_loss: 1.8699 - val_acc: 0.3160
Epoch 7/120
1/1 - 0s - loss: 1.9307 - acc: 0.3500 - val_loss: 1.8084 - val_acc: 0.3820
Epoch 8/120
1/1 - 0s - loss: 1.8068 - acc: 0.4286 - val_loss: 1.7449 - val_acc: 0.5320
Epoch 9/120
1/1 - 0s - loss: 1.7419 - acc: 0.4357 - val_loss: 1.6791 - val_acc: 0.6180
Epoch 10/120
1/1 - 0s - loss: 1.7992 - acc: 0.4429 - val_loss: 1.6142 - val_acc: 0.6160
Epoch 11/120
1/1 - 0s - loss: 1.6373 - acc: 0.5429 - val_loss: 1.5286 - val_acc: 0.6260
Epoch 12/120
1/1 - 0s - loss: 1.6104 - acc: 0.5000 - val_loss: 1.4470 - val_acc: 0.6480
Epoch 13/120
1/1 - 0s - loss: 1.5940 - acc: 0.5000 - val_loss: 1.3990 - val_acc: 0.6360
Epoch 14/120
1/1 - 0s - loss: 1.6000 - acc: 0.5286 - val_loss: 1.3676 - val_acc: 0.6400
Epoch 15/120
1/1 - 0s - loss: 1.4582 - acc: 0.5786 - val_loss: 1.3376 - val_acc: 0.6620
Epoch 16/120
1/1 - 0s - loss: 1.4981 - acc: 0.5643 - val_loss: 1.3105 - val_acc: 0.7040
Epoch 17/120
1/1 - 0s - loss: 1.4196 - acc: 0.6500 - val_loss: 1.2935 - val_acc: 0.7060
Epoch 18/120
1/1 - 0s - loss: 1.4223 - acc: 0.6286 - val_loss: 1.2826 - val_acc: 0.7040
Epoch 19/120
1/1 - 0s - loss: 1.6010 - acc: 0.5786 - val_loss: 1.2728 - val_acc: 0.7060
Epoch 20/120
1/1 - 0s - loss: 1.4398 - acc: 0.7000 - val_loss: 1.2584 - val_acc: 0.7200
Epoch 21/120
1/1 - 0s - loss: 1.3107 - acc: 0.6786 - val_loss: 1.2481 - val_acc: 0.7240
Epoch 22/120
1/1 - 0s - loss: 1.3125 - acc: 0.6714 - val_loss: 1.2400 - val_acc: 0.7180
Epoch 23/120
1/1 - 0s - loss: 1.3205 - acc: 0.6929 - val_loss: 1.2298 - val_acc: 0.7120
Epoch 24/120
1/1 - 0s - loss: 1.1782 - acc: 0.7500 - val_loss: 1.2171 - val_acc: 0.7020
Epoch 25/120
1/1 - 0s - loss: 1.2335 - acc: 0.7286 - val_loss: 1.2071 - val_acc: 0.6980
Epoch 26/120
1/1 - 0s - loss: 1.2707 - acc: 0.6714 - val_loss: 1.1907 - val_acc: 0.6980
Epoch 27/120
1/1 - 0s - loss: 1.2500 - acc: 0.6643 - val_loss: 1.1814 - val_acc: 0.7020
Epoch 28/120
1/1 - 0s - loss: 1.1690 - acc: 0.7500 - val_loss: 1.1774 - val_acc: 0.7040
Epoch 29/120
1/1 - 0s - loss: 1.3786 - acc: 0.7214 - val_loss: 1.1625 - val_acc: 0.7240
Epoch 30/120
1/1 - 0s - loss: 1.2246 - acc: 0.7429 - val_loss: 1.1497 - val_acc: 0.7360
Epoch 31/120
1/1 - 0s - loss: 1.1109 - acc: 0.7929 - val_loss: 1.1388 - val_acc: 0.7440
Epoch 32/120
1/1 - 0s - loss: 1.0982 - acc: 0.7929 - val_loss: 1.1308 - val_acc: 0.7600
Epoch 33/120
```

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

### Report Example Results

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/distmult-link-prediction.html

Displays example evaluation results in a DataFrame format, similar to those reported in the DistMult paper for comparison.

```python
results_as_dataframe(0.83, 0.942)
```

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

### Create a StellarGraph with tensor features

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-numpy.ipynb

This example demonstrates constructing a StellarGraph where one node type has tensor features and another has vector features. The `info()` method confirms the feature shape.

```python
square_foo_tensors_and_bar = StellarGraph(
    {"foo": square_foo_tensors, "bar": square_bar}, square_edges
)
print(square_foo_tensors_and_bar.info())
```

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

### Import StellarGraph and Dependencies

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/graphsage-node-classification.ipynb

Imports necessary libraries for graph analysis, machine learning, and data manipulation. Ensure these are installed before running.

```python
import networkx as nx
import pandas as pd
import os

import stellargraph as sg
from stellargraph.mapper import GraphSAGENodeGenerator
from stellargraph.layer import GraphSAGE

from tensorflow.keras import layers, optimizers, losses, metrics, Model
from sklearn import preprocessing, feature_extraction, model_selection
from stellargraph import datasets
from IPython.display import display, HTML
import matplotlib.pyplot as plt
%matplotlib inline
```

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

### Clone StellarGraph Repository

Source: https://stellargraph.readthedocs.io/en/stable/README.html

Clone the official StellarGraph GitHub repository to your local machine. This is the first step for installing from source.

```bash
git clone https://github.com/stellargraph/stellargraph.git
```

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

### Run Static Random Walks

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/ctdne-link-prediction.html

Initialize and run static random walks using StellarGraph's BiasedRandomWalk. Specify the nodes to start walks from, the number of walks per node, and the length of each walk.

```python
from stellargraph.data import BiasedRandomWalk

static_rw = BiasedRandomWalk(graph)
static_walks = static_rw.run(
    nodes=graph.nodes(), n=num_walks_per_node, length=walk_length
)

print("Number of static random walks: {}".format(len(static_walks)))
```

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

### Create a StellarGraph with multiple node types

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-numpy.ipynb

Construct a StellarGraph by combining IndexedArrays for different node types and defining edges. This example includes node types with and without features.

```python
square_foo_and_bar = StellarGraph({"foo": square_foo, "bar": square_bar}, square_edges)
print(square_foo_and_bar.info())
```

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

### Define and compile a GCN model without pre-training

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-deep-graph-infomax-fine-tuning-node-classification.ipynb

Creates and compiles a GCN model from scratch for supervised node classification. It uses the same configuration as the pre-trained model but starts with random weights.

```python
direct_gcn_model = make_gcn_model()
direct_x_in, direct_x_out = direct_gcn_model.in_out_tensors()
direct_predictions = tf.keras.layers.Dense(
    units=train_targets.shape[1], activation="softmax"
)(direct_x_out)

direct_model = Model(inputs=direct_x_in, outputs=direct_predictions)
direct_model.compile(
    optimizer=optimizers.Adam(lr=0.01), loss="categorical_crossentropy", metrics=["acc"],
)
```

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

### Sample Positive and Negative Links

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/ctdne-link-prediction.html

Use this function to generate positive and negative link examples from a graph and a set of edges. It requires the graph object and the edges designated for the train/test split. Ensure `random` is imported.

```python
def positive_and_negative_links(g, edges):
    pos = list(edges["source", "target"].itertuples(index=False))
    neg = sample_negative_examples(g, pos)
    return pos, neg

def sample_negative_examples(g, positive_examples):
    positive_set = set(positive_examples)

    def valid_neg_edge(src, tgt):
        return (
            # no self-loops
            src != tgt
            and
            # neither direction of the edge should be a positive one
            (src, tgt) not in positive_set
            and (tgt, src) not in positive_set
        )

    possible_neg_edges = [
        (src, tgt) for src in g.nodes() for tgt in g.nodes() if valid_neg_edge(src, tgt)
    ]
    return random.sample(possible_neg_edges, k=len(positive_examples))
```

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

### Set training parameters for HinSAGE model

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html

Configures the minibatch size, number of training epochs, and the split ratio for training and testing data. This setup is crucial for controlling the training process and evaluating model performance.

```python
batch_size = 200
epochs = 20
# Use 70% of edges for training, the rest for testing:
train_size = 0.7
test_size = 0.3
```

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

### Initialize DistMult Model and Compile

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/distmult-link-prediction.html

Initializes the KGTripleGenerator and DistMult model, then compiles the model for training. Ensure embedding_dimension is set.

```python
wn18_gen = KGTripleGenerator(
    wn18_graph, batch_size=len(wn18_train) // 10  # ~10 batches per epoch
)

wn18_distmult = DistMult(
    wn18_gen,
    embedding_dimension=embedding_dimension,
    embeddings_regularizer=regularizers.l2(1e-7),
)

wn18_inp, wn18_out = wn18_distmult.in_out_tensors()

wn18_model = Model(inputs=wn18_inp, outputs=wn18_out)

wn18_model.compile(
    optimizer=optimizers.Adam(lr=0.001),
    loss=losses.BinaryCrossentropy(from_logits=True),
    metrics=[metrics.BinaryAccuracy(threshold=0.0)],
)
```

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

### Importing Required Libraries

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/ppnp-node-classification.ipynb

Initializes the environment by importing necessary libraries including TensorFlow, StellarGraph, and scikit-learn.

```python
import networkx as nx
import pandas as pd
import numpy as np
import os
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

import stellargraph as sg
from stellargraph.mapper import FullBatchNodeGenerator
from stellargraph.layer.ppnp import PPNP
from stellargraph.layer.appnp import APPNP

from tensorflow.keras import layers, optimizers, losses, metrics, Model
from sklearn import preprocessing, feature_extraction, model_selection
from stellargraph import datasets
from IPython.display import display, HTML
import matplotlib.pyplot as plt
%matplotlib inline
```

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

### Get Label Count

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/graphsage-inductive-node-classification.ipynb

Returns the total number of labels in the dataset.

```python
len(labels)
```

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

### Load the Blog Catalog dataset

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/metapath2vec-link-prediction.ipynb

Loads the heterogeneous Blog Catalog 3 dataset and displays its description.

```python
dataset = datasets.BlogCatalog3()
display(HTML(dataset.description))
graph = dataset.load()
```

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

### Get Prediction Shape

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/cluster-gcn-node-classification.ipynb

Check the shape of the generated predictions. The output should be (number_of_nodes, number_of_classes).

```python
all_predictions.shape
```

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

### Get Sampled Label Count

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/graphsage-inductive-node-classification.ipynb

Returns the count of labels in the sampled training set.

```python
len(labels_sampled)
```

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

### Import required libraries

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gat-node-classification.ipynb

Initializes the environment by importing necessary libraries for graph processing, machine learning, and visualization.

```python
import networkx as nx
import pandas as pd
import os

import stellargraph as sg
from stellargraph.mapper import FullBatchNodeGenerator
from stellargraph.layer import GAT

from tensorflow.keras import layers, optimizers, losses, metrics, Model
from sklearn import preprocessing, feature_extraction, model_selection
from stellargraph import datasets
from IPython.display import display, HTML
import matplotlib.pyplot as plt
%matplotlib inline
```

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

### Get Node IDs from Subgraph

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/attri2vec-link-prediction.html

Retrieves and sorts the node identifiers from a NetworkX graph.

```python
subgraph_node_ids = sorted(list(G_sub_nx.nodes))
```

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

### Initialize Neo4j connection

Source: https://stellargraph.readthedocs.io/en/stable/demos/connector/neo4j/load-cora-into-neo4j.ipynb

Sets up the py2neo Graph object using environment variables.

```python
import time
import py2neo

default_host = os.environ.get("STELLARGRAPH_NEO4J_HOST")

# Create the Neo4j Graph database object; the arguments can be edited to specify location and authentication
graph = py2neo.Graph(host=default_host, port=None, user=None, password=None)
```

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

### Split Graph for Testing

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/homogeneous-comparison-link-prediction.ipynb

Performs an initial train/test split on a graph to isolate test examples.

```python
graph_test, examples_test, labels_test = edge_splitter_test.train_test_split(
    p=0.1, method="global"
)

print(graph_test.info())
```

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

### Import StellarGraph Library

Source: https://stellargraph.readthedocs.io/en/stable/demos/time-series/gcn-lstm-time-series.ipynb

Initializes the StellarGraph library for data loading.

```python
import stellargraph as sg
```

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

### Displaying Cora citation edges

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-pandas.ipynb

Example format of the cora.cites TSV file showing citation relationships.

```text
35  1033
35  103482
35  103515
...
```

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

### Initialize and Compile ComplEx Model

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/complex-link-prediction.html

Sets up the KGTripleGenerator and ComplEx model, then compiles it with Keras optimizers and loss functions.

```python
wn18_gen = KGTripleGenerator(
    wn18_graph, batch_size=len(wn18_train) // 100  # ~100 batches per epoch
)

wn18_complex = ComplEx(
    wn18_gen,
    embedding_dimension=embedding_dimension,
    embeddings_regularizer=regularizers.l2(1e-7),
)

wn18_inp, wn18_out = wn18_complex.in_out_tensors()

wn18_model = Model(inputs=wn18_inp, outputs=wn18_out)

wn18_model.compile(
    optimizer=optimizers.Adam(lr=0.001),
    loss=losses.BinaryCrossentropy(from_logits=True),
    metrics=[metrics.BinaryAccuracy(threshold=0.0)],
)
```

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

### Get Number of Nodes

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/cluster-gcn-node-classification.ipynb

Determine the total number of nodes processed by the generator, which corresponds to the number of predictions made.

```python
len(all_gen.node_order)
```

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

### Configure Training Callbacks

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/sgc-node-classification.ipynb

Sets up early stopping and model checkpointing to save the best weights during training.

```python
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint

if not os.path.isdir("logs"):
    os.makedirs("logs")
es_callback = EarlyStopping(
    monitor="val_acc", patience=50
)  # patience is the number of epochs to wait before early stopping in case of no further improvement
mc_callback = ModelCheckpoint(
    "logs/best_model.h5", monitor="val_acc", save_best_only=True, save_weights_only=True
)
```

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

### Initialize KGTripleGenerator and DistMult Model

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/distmult-link-prediction.html

Sets up a KGTripleGenerator for batching graph data and initializes a DistMult model for knowledge graph embeddings. The model is configured with a specified embedding dimension and L2 regularization.

```python
fb15k_gen = KGTripleGenerator(
    fb15k_graph, batch_size=len(fb15k_train) // 10  # ~100 batches per epoch
)

fb15k_distmult = DistMult(
    fb15k_gen,
    embedding_dimension=embedding_dimension,
    embeddings_regularizer=regularizers.l2(1e-8),
)

fb15k_inp, fb15k_out = fb15k_distmult.in_out_tensors()
```

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

### Create and Fit Model with Data Generator

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/watch-your-step-embeddings.ipynb

Instantiate a data generator and fit the model. Ensure the `steps_per_epoch` is correctly calculated based on the graph size and batch size.

```python
batch_size = 10
train_gen = generator.flow(batch_size=batch_size, num_parallel_calls=10)

history = model.fit(
    train_gen, epochs=epochs, verbose=1, steps_per_epoch=int(len(G.nodes()) // batch_size)
)
```

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

### Get Model Input and Output Tensors

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/keras-node2vec-embeddings.ipynb

Retrieve the input and output tensors for the Node2Vec model, which will be used to build the Keras model.

```python
x_inp, x_out = node2vec.in_out_tensors()
```

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

### Displaying Cora node content

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-pandas.ipynb

Example format of the cora.content TSV file containing node IDs, word vectors, and subject classes.

```text
31336   0   0   ... 0   1   0   0   0   0   0   0   Neural_Networks
1061127 0   0   ... 1   0   0   0   0   0   0   0   Rule_Learning
1106406 0   0   ... 0   0   0   0   0   0   0   0   Reinforcement_Learning
...
```

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

### Initialize StellarGraph from edges

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-pandas.ipynb

Construct a basic StellarGraph instance using a DataFrame of edges.

```python
square = StellarGraph(edges=square_edges)
```

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

### Get Node Features for Subgraph

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/attri2vec-link-prediction.html

Selects and reindexes node features based on the sorted node IDs of the subgraph. Requires `node_data` and `feature_names` to be pre-defined.

```python
subgraph_node_features = node_data[feature_names].reindex(subgraph_node_ids)
```

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

### Identify GAT Embedding Layer

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gat-node-classification.ipynb

Finds the first Keras layer starting with 'graph_attention' to use as the embedding layer. Preserves previously trained weights.

```python
emb_layer = next(l for l in model.layers if l.name.startswith("graph_attention"))
print(
    "Embedding layer: {}, output shape {}".format(emb_layer.name, emb_layer.output_shape)
)
```

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

### Download and prepare BlogCatalog3 dataset

Source: https://stellargraph.readthedocs.io/en/stable/demos/zzz-internal-developers/graph-resource-usage.ipynb

Downloads the BlogCatalog3 dataset and defines the file paths for edges, group edges, groups, and nodes. This prepares the data for loading.

```python
blogcatalog3 = sg.datasets.BlogCatalog3()
blogcatalog3.download()

blogcatalog3_edges = os.path.join(blogcatalog3.data_directory, "edges.csv")
blogcatalog3_group_edges = os.path.join(blogcatalog3.data_directory, "group-edges.csv")
blogcatalog3_groups = os.path.join(blogcatalog3.data_directory, "groups.csv")
blogcatalog3_nodes = os.path.join(blogcatalog3.data_directory, "nodes.csv")
```

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

### Import required libraries

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-node-classification.ipynb

Initializes the environment by importing StellarGraph, TensorFlow Keras, and standard data science libraries.

```python
import pandas as pd
import os

import stellargraph as sg
from stellargraph.mapper import FullBatchNodeGenerator
from stellargraph.layer import GCN

from tensorflow.keras import layers, optimizers, losses, metrics, Model
from sklearn import preprocessing, model_selection
from IPython.display import display, HTML
import matplotlib.pyplot as plt
%matplotlib inline
```

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

### Import Libraries for Node2Vec

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/keras-node2vec-embeddings.ipynb

Imports necessary libraries for Node2Vec representation learning, including Keras, NetworkX, Pandas, and Stellargraph components. Ensure these are installed before running.

```python
import matplotlib.pyplot as plt

from sklearn.manifold import TSNE

import os
import networkx as nx
import numpy as np
import pandas as pd
from tensorflow import keras

from stellargraph import StellarGraph
from stellargraph.data import BiasedRandomWalk
from stellargraph.data import UnsupervisedSampler
from stellargraph.data import BiasedRandomWalk
from stellargraph.mapper import Node2VecLinkGenerator, Node2VecNodeGenerator
from stellargraph.layer import Node2Vec, link_classification

from stellargraph import datasets
from IPython.display import display, HTML

%matplotlib inline
```

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

### Import NetworkX

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-networkx.ipynb

Import the NetworkX library for graph creation.

```python
import networkx as nx
```

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

### Filter Nodes with Cypher WHERE Clause

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-saving-neo4j.ipynb

Use a Cypher WHERE clause to select nodes based on their properties. This example filters nodes where 'left' is true or 'top' is false.

```python
raw_subgraph_nodes = neo4j_graph.run(
    """
    MATCH (n)
    WHERE n.left OR NOT n.top
    RETURN n.name AS name, n.left, n.top
    """
).to_data_frame()

subgraph_nodes = raw_subgraph_nodes.set_index("name")
subgraph_nodes
```

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

### Initialize UnsupervisedSampler

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/graphsage-unsupervised-sampler-embeddings.ipynb

Create an instance of UnsupervisedSampler using the graph and configured parameters.

```python
unsupervised_samples = UnsupervisedSampler(
    G, nodes=nodes, length=length, number_of_walks=number_of_walks
)
```

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

### Extract k-hop nodes using Cypher

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-saving-neo4j.ipynb

Uses a variable length relationship constraint to retrieve nodes within a specified hop distance from start nodes.

```python
start_nodes = ["b"]

raw_hop_nodes = neo4j_graph.run(
    """
    MATCH (start) -[*0..1]- (n)
    WHERE start.name IN $start_nodes
    WITH DISTINCT n
    RETURN n.name AS name, n.top, n.left
    """,
    {"start_nodes": start_nodes},
).to_data_frame()

hop_nodes = raw_hop_nodes.set_index("name")
hop_nodes
```

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

### Train Link Prediction Classifier

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/node2vec-link-prediction.ipynb

Trains a logistic regression classifier for link prediction. Requires pre-defined link examples, labels, an embedding function, and a binary operator.

```python
def train_link_prediction_model(
    link_examples,
    link_labels,
    get_embedding,
    binary_operator
):
    clf = link_prediction_classifier()
    link_features = link_examples_to_features(
        link_examples, get_embedding, binary_operator
    )
    clf.fit(link_features, link_labels)
    return clf
```

```python
def link_prediction_classifier(max_iter=2000):
    lr_clf = LogisticRegressionCV(Cs=10, cv=10, scoring="roc_auc", max_iter=max_iter)
    return Pipeline(steps=[("sc", StandardScaler()), ("clf", lr_clf)])
```

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

### Define Link Prediction Classifier Helpers

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/ctdne-link-prediction.html

Provides functions to transform link examples into features, initialize a logistic regression pipeline, and evaluate performance using ROC AUC.

```python
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import StandardScaler


def link_examples_to_features(link_examples, transform_node):
    op_func = (
        operator_func[binary_operator]
        if isinstance(binary_operator, str)
        else binary_operator
    )
    return [
        op_func(transform_node(src), transform_node(dst)) for src, dst in link_examples
    ]


def link_prediction_classifier(max_iter=2000):
    lr_clf = LogisticRegressionCV(Cs=10, cv=10, scoring="roc_auc", max_iter=max_iter)
    return Pipeline(steps=[("sc", StandardScaler()), ("clf", lr_clf)])


def evaluate_roc_auc(clf, link_features, link_labels):
    predicted = clf.predict_proba(link_features)

    # check which class corresponds to positive links
    positive_column = list(clf.classes_).index(1)
    return roc_auc_score(link_labels, predicted[:, positive_column])
```

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

### Initialize StellarGraph with Node and Edge Features

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-pandas.ipynb

Constructs a StellarGraph with both node and edge features defined in their respective DataFrames.

```python
square_named_features = StellarGraph(
    {"corner": square_node_data}, {"line": square_edge_data}
)
print(square_named_features.info())
```

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

### Compute Node Features from Node ID

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-networkx.ipynb

Computes numerical features for nodes based on their ID. This is a basic example; real-world scenarios might involve more complex feature calculations.

```python
g_feature_attr = g.copy()

def compute_features(node_id):
    # in general this could compute something based on other features, but for this example,
    # we don't have any other features, so we'll just do something basic with the node_id
    return [ord(node_id), len(node_id)]


for node_id, node_data in g_feature_attr.nodes(data=True):
    node_data["feature"] = compute_features(node_id)

# let's see what some of them look like:
g_feature_attr.nodes["a"], g_feature_attr.nodes["c"]
```

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

### Import required libraries

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/cluster-gcn-node-classification.ipynb

Initializes the environment by importing necessary libraries for data manipulation, graph processing, and model building.

```python
import networkx as nx
import pandas as pd
import itertools
import json
import os

import numpy as np

from networkx.readwrite import json_graph

from sklearn.preprocessing import StandardScaler

import stellargraph as sg
from stellargraph.mapper import ClusterNodeGenerator
from stellargraph.layer import GCN
from stellargraph import globalvar

from tensorflow.keras import backend as K

from tensorflow.keras import layers, optimizers, losses, metrics, Model
from sklearn import preprocessing, feature_extraction, model_selection
from stellargraph import datasets
from IPython.display import display, HTML
from IPython.display import display, HTML
import matplotlib.pyplot as plt
%matplotlib inline
```

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

### Configure and Train APPNP Model

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/ppnp-node-classification.ipynb

Initializes the FullBatchNodeGenerator with GCN method, defines the APPNP model architecture, and executes the training process with early stopping and model checkpointing.

```python
generator = FullBatchNodeGenerator(G, method="gcn", sparse=True)

train_gen = generator.flow(train_subjects.index, train_targets)
val_gen = generator.flow(val_subjects.index, val_targets)
test_gen = generator.flow(test_subjects.index, test_targets)

appnp = APPNP(
    layer_sizes=[64, 64, train_targets.shape[-1]],
    activations=["relu", "relu", "relu"],
    bias=True,
    generator=generator,
    teleport_probability=0.1,
    dropout=0.5,
    kernel_regularizer=keras.regularizers.l2(0.001),
)

x_inp, x_out = appnp.in_out_tensors()
predictions = keras.layers.Softmax()(x_out)

appnp_model = keras.models.Model(inputs=x_inp, outputs=predictions)
appnp_model.compile(
    loss="categorical_crossentropy",
    metrics=["acc"],
    optimizer=keras.optimizers.Adam(lr=0.01),
)

es_callback = EarlyStopping(
    monitor="val_acc", patience=50
)  # patience is the number of epochs to wait before early stopping in case of no further improvement

mc_callback = ModelCheckpoint(
    "logs/best_appnp_model.h5",
    monitor="val_acc",
    save_best_only=True,
    save_weights_only=True,
)

history = appnp_model.fit(
    train_gen,
    epochs=120,
    validation_data=val_gen,
    verbose=2,
    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph
    callbacks=[es_callback, mc_callback],
)
```

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

### Initialize FullBatchNodeGenerator for PPNP

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/ppnp-node-classification.ipynb

Use `FullBatchNodeGenerator` with `method='ppnp'` to preprocess the adjacency matrix and supply the personalized page rank matrix. Ensure `sparse=False` for dense matrices and set `teleport_probability` for the propagation step.

```python
generator = FullBatchNodeGenerator(
    G, method="ppnp", sparse=False, teleport_probability=0.1
)
```

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

### Create a StellarGraph object

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-pandas.ipynb

Initialize a StellarGraph instance using node features and edge data.

```python
cora_no_subject = StellarGraph({"paper": cora_content_no_subject}, {"cites": cora_cites})
print(cora_no_subject.info())
```

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

### Load the BlogCatalog3 dataset

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/metapath2vec-embeddings.ipynb

Load the graph dataset and display its basic properties.

```python
dataset = datasets.BlogCatalog3()
display(HTML(dataset.description))
g = dataset.load()
print(
    "Number of nodes {} and number of edges {} in graph.".format(
        g.number_of_nodes(), g.number_of_edges()
    )
)
```

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

### Create Unsupervised Sampler

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/keras-node2vec-embeddings.ipynb

Initialize the UnsupervisedSampler with the graph, a list of nodes, and the configured biased random walker.

```python
unsupervised_samples = UnsupervisedSampler(G, nodes=list(G.nodes()), walker=walker)
```

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

### Select Target Node and Get True/Predicted Labels

Source: https://stellargraph.readthedocs.io/en/stable/demos/interpretability/gcn-sparse-node-link-importance.html

Identify the target node for interpretation and retrieve its true class label and predicted scores. Ensure the generator is set up to flow graph nodes.

```python
graph_nodes = list(G.nodes())
target_nid = 1109199
target_idx = graph_nodes.index(target_nid)
y_true = all_targets[target_idx]  # true class of the target node
```

```python
all_gen = generator.flow(graph_nodes)
y_pred = model.predict(all_gen)[0, target_idx]
class_of_interest = np.argmax(y_pred)

print(
    "Selected node id: {}, \nTrue label: {}, \nPredicted scores: {}".format(
        target_nid,
        y_true,
        y_pred.round(2)
    )
)
```

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

### Create Unsupervised Sampler

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/attri2vec-link-prediction.html

Initializes an UnsupervisedSampler for Attri2Vec, which generates random walks on the graph for training.

```python
unsupervised_samples = UnsupervisedSampler(
    G_sub, nodes=nodes, length=length, number_of_walks=number_of_walks
)
```

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

### Get Node Embeddings from Node IDs

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/keras-node2vec-embeddings.ipynb

Generates node embeddings from node IDs using a Node2VecNodeGenerator and the previously defined embedding model. Ensure the Node2VecNodeGenerator is properly configured with the graph and batch size.

```python
node_gen = Node2VecNodeGenerator(G, batch_size).flow(subjects.index)
node_embeddings = embedding_model.predict(node_gen, workers=4, verbose=1)
```

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

### Compile and Train Keras Model

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/graphsage-inductive-node-classification.ipynb

Configures the Keras model with an optimizer and loss function, then executes training.

```python
model = Model(inputs=x_inp, outputs=prediction)
model.compile(
    optimizer=optimizers.Adam(lr=0.005),
    loss=losses.categorical_crossentropy,
    metrics=["acc"],
)
```

```python
val_gen = generator.flow(val_labels.index, val_targets)
```

```python
history = model.fit(
    train_gen, epochs=15, validation_data=val_gen, verbose=0, shuffle=False
)
```

```python
sg.utils.plot_history(history)
```

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

### Initialize FullBatchNodeGenerator

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-node-classification.ipynb

Creates a generator for the GCN algorithm using the normalized graph Laplacian matrix.

```python
generator = FullBatchNodeGenerator(G, method="gcn")
```

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

### Helper Function for Link Feature Extraction

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/node2vec-link-prediction.ipynb

Defines a helper function to convert link examples (pairs of nodes) into feature vectors. It applies a specified binary operator to the node embeddings of the source and destination nodes.

```python
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import StandardScaler


# 1. link embeddings
def link_examples_to_features(link_examples, transform_node, binary_operator):
    return [
        binary_operator(transform_node(src), transform_node(dst))
        for src, dst in link_examples
    ]
```

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

### Create Link Generators

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/graphsage-link-prediction.html

Initializes link generators for training and testing, and creates data flows.

```python
train_gen = GraphSAGELinkGenerator(G_train, batch_size, num_samples)
train_flow = train_gen.flow(edge_ids_train, edge_labels_train, shuffle=True)
```

```python
test_gen = GraphSAGELinkGenerator(G_test, batch_size, num_samples)
test_flow = test_gen.flow(edge_ids_test, edge_labels_test)
```

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

### Create the training generator

Source: https://stellargraph.readthedocs.io/en/stable/demos/interpretability/gcn-sparse-node-link-importance.html

Prepares the training data flow for the GCN model.

```python
train_gen = generator.flow(train_subjects.index, train_targets)
```

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

### Get Schema Type Adjacency List

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html

Retrieves the type adjacency list from the generator's schema for the specified head node types and number of samples. This helps understand the graph structure relevant to the model.

```python
generator.schema.type_adjacency_list(generator.head_node_types, len(num_samples))
```

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

### Display model summary

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html

Prints the architecture and layer details of the compiled model.

```python
model.summary()
```

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

### Import necessary libraries for Node2Vec node classification

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/keras-node2vec-node-classification.ipynb

Imports essential libraries for data manipulation, machine learning, and graph analysis, including Stellargraph components for Node2Vec and Keras for model implementation. Ensure these are installed before running.

```python
import matplotlib.pyplot as plt

from sklearn.manifold import TSNE
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import accuracy_score

import os
import networkx as nx
import numpy as np
import pandas as pd
from tensorflow import keras

from stellargraph import StellarGraph
from stellargraph.data import BiasedRandomWalk
from stellargraph.data import UnsupervisedSampler
from stellargraph.data import BiasedRandomWalk
from stellargraph.mapper import Node2VecLinkGenerator, Node2VecNodeGenerator
from stellargraph.layer import Node2Vec, link_classification

from stellargraph import datasets
from IPython.display import display, HTML

%matplotlib inline
```

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

### Initialize StellarGraph with Node Features

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-pandas.ipynb

Constructs a StellarGraph object using a node feature DataFrame and an edge list.

```python
square_node_features = StellarGraph(square_node_data, square_edges)
print(square_node_features.info())
```

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

### Download StellarGraph Demos

Source: https://stellargraph.readthedocs.io/en/stable/demos/index.html

Use this curl command to download a local copy of all StellarGraph demo notebooks. The archive is extracted and the master directory is stripped.

```bash
curl -L https://github.com/stellargraph/stellargraph/archive/master.zip | tar -xz --strip=1 stellargraph-master/demos
```

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

### Initialize data generators

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/gcn-deep-graph-infomax-fine-tuning-node-classification.ipynb

Creates a FullBatchNodeGenerator and a CorruptedGenerator for unsupervised training.

```python
fullbatch_generator = FullBatchNodeGenerator(G)

corrupted_generator = CorruptedGenerator(fullbatch_generator)
gen = corrupted_generator.flow(G.nodes())
```

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

### Run Temporal Random Walks

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/ctdne-link-prediction.html

Initialize and run temporal random walks using StellarGraph's TemporalRandomWalk. Specify the number of context windows, context window size, maximum walk length, and walk bias.

```python
from stellargraph.data import TemporalRandomWalk

temporal_rw = TemporalRandomWalk(graph)
temporal_walks = temporal_rw.run(
    num_cw=num_cw,
    cw_size=context_window_size,
    max_walk_length=walk_length,
    walk_bias="exponential",
)

print("Number of temporal random walks: {}".format(len(temporal_walks)))
```

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

### Train and Evaluate Model

Source: https://stellargraph.readthedocs.io/en/stable/demos/time-series/gcn-lstm-time-series.ipynb

Fit the model to training data and display the summary and performance metrics.

```python
history = model.fit(
    trainX,
    trainY,
    epochs=100,
    batch_size=60,
    shuffle=True,
    verbose=0,
    validation_data=[testX, testY],
)
```

```python
model.summary()
```

```python
print(
    "Train loss: ",
    history.history["loss"][-1],
    "\nTest loss:",
    history.history["val_loss"][-1],
)
```

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

### Configure Early Stopping and Model Checkpointing

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/ppnp-node-classification.ipynb

Set up callbacks for early stopping based on validation accuracy and for saving the best performing model weights during training. Patience determines the number of epochs to wait for improvement.

```python
es_callback = EarlyStopping(
    monitor="val_acc", patience=50
)  # patience is the number of epochs to wait before early stopping in case of no further improvement

mc_callback = ModelCheckpoint(
    "logs/best_fc_model.h5",
    monitor="val_acc",
    save_best_only=True,
    save_weights_only=True,
)
```

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

### Configure WatchYourStep model

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/watch-your-step-embeddings.ipynb

Initializes the WatchYourStep layer with specified embedding dimensions and regularization.

```python
wys = WatchYourStep(
    generator,
    num_walks=80,
    embedding_dimension=128,
    attention_regularizer=regularizers.l2(0.5),
)
x_in, x_out = wys.in_out_tensors()
```

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

### Import required libraries

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/graphwave-embeddings.ipynb

Load necessary libraries for graph manipulation, machine learning, and visualization.

```python
import networkx as nx
from stellargraph.mapper import GraphWaveGenerator
from stellargraph import StellarGraph
from sklearn.decomposition import PCA
import numpy as np
from matplotlib import pyplot as plt
from scipy.sparse.linalg import eigs
import tensorflow as tf
from tensorflow.keras import backend as K
```

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

### Connect to Neo4j instance

Source: https://stellargraph.readthedocs.io/en/stable/demos/basics/loading-saving-neo4j.ipynb

Establish a connection to a running Neo4j database using the py2neo library.

```python
import os
import py2neo

default_host = os.environ.get("STELLARGRAPH_NEO4J_HOST")

# Create the Neo4j Graph database object; the parameters can be edited to specify location and authentication
neo4j_graph = py2neo.Graph(host=default_host, port=None, user=None, password=None)
```

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

### Generate random walks

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/metapath2vec-embeddings.ipynb

Initialize the random walker and generate the corpus of walks.

```python
from stellargraph.data import UniformRandomMetaPathWalk

# Create the random walker
rw = UniformRandomMetaPathWalk(g)

walks = rw.run(
    nodes=list(g.nodes()),  # root nodes
    length=walk_length,  # maximum length of a random walk
    n=1,  # number of random walks per root node
    metapaths=metapaths,  # the metapaths
)

print("Number of random walks: {}".format(len(walks)))
```

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

### Initialize UnsupervisedSampler

Source: https://stellargraph.readthedocs.io/en/stable/demos/embeddings/attri2vec-embeddings.ipynb

Specify parameters for creating an UnsupervisedSampler instance, including root nodes, number of walks, and walk length.

```python
nodes = list(G.nodes())
number_of_walks = 4
length = 5
```

```python
unsupervised_samples = UnsupervisedSampler(
    G, nodes=nodes, length=length, number_of_walks=number_of_walks
)
```

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

### Create Data Generators for Training and Validation

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/distmult-link-prediction.html

Creates data generators for training and validation using the KGTripleGenerator.flow method. The negative_samples parameter controls the ratio of negative to positive edges.

```python
wn18_train_gen = wn18_gen.flow(
    wn18_train, negative_samples=negative_samples, shuffle=True
)
wn18_valid_gen = wn18_gen.flow(wn18_valid, negative_samples=negative_samples)
```

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

### Initialize Knowledge Graph Data Generator and Model

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/complex-link-prediction.html

Sets up the data generator for the knowledge graph and defines the ComplEx model architecture with L2 regularization. The model is then compiled with an Adam optimizer and binary crossentropy loss.

```python
fb15k_gen = KGTripleGenerator(
    fb15k_graph, batch_size=len(fb15k_train) // 100  # ~100 batches per epoch
)

fb15k_complex = ComplEx(
    fb15k_gen,
    embedding_dimension=embedding_dimension,
    embeddings_regularizer=regularizers.l2(1e-8),
)

fb15k_inp, fb15k_out = fb15k_complex.in_out_tensors()

fb15k_model = Model(inputs=fb15k_inp, outputs=fb15k_out)
fb15k_model.compile(
    optimizer=optimizers.Adam(lr=0.001),
    loss=losses.BinaryCrossentropy(from_logits=True),
    metrics=[metrics.BinaryAccuracy(threshold=0.0)],
)
```

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

### Create Log Directory

Source: https://stellargraph.readthedocs.io/en/stable/demos/node-classification/ppnp-node-classification.ipynb

Ensures that a 'logs' directory exists for storing model checkpoints and other log files. This is a prerequisite for saving model weights.

```python
if not os.path.isdir("logs"):
    os.makedirs("logs")
```

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

### Load MovieLens Dataset

Source: https://stellargraph.readthedocs.io/en/stable/demos/link-prediction/hinsage-link-prediction.html

Loads the MovieLens dataset and displays its description. This is the initial step for working with graph data.

```python
dataset = datasets.MovieLens()
display(HTML(dataset.description))
G, edges_with_ratings = dataset.load()
```