"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example demonstrates how to apply components from the stellargraph library to perform representation learning via Node2Vec. This uses a keras implementation of Node2Vec available in stellargraph instead of the reference implementation provided by ``gensim``. This implementation provides flexible interfaces to downstream tasks for end-to-end learning.\n",
"\n",
"\n",
"**References**\n",
"\n",
"[1] Node2Vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. ([link](https://snap.stanford.edu/node2vec/))\n",
"\n",
"[2] Distributed representations of words and phrases and their compositionality. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. In Advances in Neural Information Processing Systems (NIPS), pp. 3111-3119, 2013. ([link](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf))\n",
"\n",
"[3] word2vec Parameter Learning Explained. X. Rong. arXiv preprint arXiv:1411.2738. 2014 Nov 11. ([link](https://arxiv.org/pdf/1411.2738.pdf))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"Following word2vec [2,3], for each (``target``,``context``) node pair $(v_i,v_j)$ collected from random walks, we learn the representation for the target node $v_i$ by using it to predict the existence of context node $v_j$, with the following three-layer neural network."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Node $v_i$'s representation in the hidden layer is obtained by multiplying $v_i$'s one-hot representation in the input layer with the input-to-hidden weight matrix $W_{in}$, which is equivalent to look up the $i$th row of input-to-hidden weight matrix $W_{in}$. The existence probability of each node conditioned on node $v_i$ is outputted in the output layer, which is obtained by multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation. To capture the ``target-context`` relation between $v_i$ and $v_j$, we need to maximize the probability $\\mathrm{P}(v_j|v_i)$. However, computing $\\mathrm{P}(v_j|v_i)$ is time consuming, which involves the matrix multiplication between $v_i$'s hidden-layer representation and the hidden-to-out weight matrix $W_{out}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To speed up the computing, we adopt the negative sampling strategy [2,3]. For each (``target``, ``context``) node pair, we sample a negative node $v_k$, which is not $v_i$'s context. To obtain the output, instead of multiplying $v_i$'s hidden-layer representation with the hidden-to-out weight matrix $W_{out}$ followed by a softmax activation, we only calculate the dot product between $v_i$'s hidden-layer representation and the $j$th column as well as the $k$th column of the hidden-to-output weight matrix $W_{out}$ followed by a sigmoid activation respectively. According to [3], the original objective to maximize $\\mathrm{P}(v_j|v_i)$ can be approximated by minimizing the cross entropy between $v_j$ and $v_k$'s outputs and their ground-truth labels (1 for $v_j$ and 0 for $v_k$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following [2,3], we denote the rows of the input-to-hidden weight matrix $W_{in}$ as ``input_embeddings`` and the columns of the hidden-to-out weight matrix $W_{out}$ as ``output_embeddings``. To build the Node2Vec model, we need look up ``input_embeddings`` for target nodes and ``output_embeddings`` for context nodes and calculate their inner product together with a sigmoid activation."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"nbsphinx": "hidden",
"tags": [
"CloudRunner"
]
},
"outputs": [],
"source": [
"# install StellarGraph if running on Google Colab\n",
"import sys\n",
"if 'google.colab' in sys.modules:\n",
" %pip install -q stellargraph[demos]==1.1.0"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"nbsphinx": "hidden",
"tags": [
"VersionCheck"
]
},
"outputs": [],
"source": [
"# verify that we're using the correct version of StellarGraph for this notebook\n",
"import stellargraph as sg\n",
"\n",
"try:\n",
" sg.utils.validate_notebook_version(\"1.1.0\")\n",
"except AttributeError:\n",
" raise ValueError(\n",
" f\"This notebook requires StellarGraph version 1.1.0, but a different version {sg.__version__} is installed. Please see .\"\n",
" ) from None"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.manifold import TSNE\n",
"\n",
"import os\n",
"import networkx as nx\n",
"import numpy as np\n",
"import pandas as pd\n",
"from tensorflow import keras\n",
"\n",
"from stellargraph import StellarGraph\n",
"from stellargraph.data import BiasedRandomWalk\n",
"from stellargraph.data import UnsupervisedSampler\n",
"from stellargraph.data import BiasedRandomWalk\n",
"from stellargraph.mapper import Node2VecLinkGenerator, Node2VecNodeGenerator\n",
"from stellargraph.layer import Node2Vec, link_classification\n",
"\n",
"from stellargraph import datasets\n",
"from IPython.display import display, HTML\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset\n",
"\n",
"\n",
"For clarity, we use only the largest connected component, ignoring isolated nodes and subgraphs; having these in the data does not prevent the algorithm from running and producing valid results."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dataset = datasets.Cora()\n",
"display(HTML(dataset.description))\n",
"G, subjects = dataset.load(largest_connected_component_only=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"StellarGraph: Undirected multigraph\n",
" Nodes: 2485, Edges: 5209\n",
"\n",
" Node types:\n",
" paper: [2485]\n",
" Features: float32 vector, length 1433\n",
" Edge types: paper-cites->paper\n",
"\n",
" Edge types:\n",
" paper-cites->paper: [5209]\n",
" Weights: all 1 (default)\n"
]
}
],
"source": [
"print(G.info())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The Node2Vec algorithm\n",
"\n",
"The Node2Vec algorithm introduced in [[1]](#refs) is a 2-step representation learning algorithm. The two steps are:\n",
"\n",
"1. Use random walks to generate sentences from a graph. A sentence is a list of node ids. The set of all sentences makes a corpus.\n",
"\n",
"2. The corpus is then used to learn an embedding vector for each node in the graph. Each node id is considered a unique word/token in a dictionary that has size equal to the number of nodes in the graph. The Word2Vec algorithm [[2]](#refs) is used for calculating the embedding vectors.\n",
"\n",
"In this implementation, we train the Node2Vec algorithm in the following two steps:\n",
"\n",
"1. Generate a set of (`target`, `context`) node pairs through starting the biased random walk with a fixed length at per node. The starting nodes are taken as the target nodes and the following nodes in biased random walks are taken as context nodes. For each (`target`, `context`) node pair, we generate 1 negative node pair.\n",
"\n",
"2. Train the Node2Vec algorithm through minimizing cross-entropy loss for `target-context` pair prediction, with the predictive value obtained by performing the dot product of the 'input embedding' of the target node and the 'output embedding' of the context node, followed by a sigmoid activation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specify the optional parameter values: the number of walks to take per node, the length of each walk. Here, to guarantee the running efficiency, we respectively set `walk_number` and `walk_length` to 100 and 5. Larger values can be set to them to achieve better performance."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"walk_number = 100\n",
"walk_length = 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the biased random walker to perform context node sampling, with the specified parameters."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"walker = BiasedRandomWalk(\n",
" G,\n",
" n=walk_number,\n",
" length=walk_length,\n",
" p=0.5, # defines probability, 1/p, of returning to source node\n",
" q=2.0, # defines probability, 1/q, for moving to a node away from the source node\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the UnsupervisedSampler instance with the biased random walker."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"unsupervised_samples = UnsupervisedSampler(G, nodes=list(G.nodes()), walker=walker)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the batch size and the number of epochs."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 50\n",
"epochs = 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define an attri2vec training generator, which generates a batch of (index of target node, index of context node, label of node pair) pairs per iteration."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"generator = Node2VecLinkGenerator(G, batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build the Node2Vec model, with the dimension of learned node representations set to 128."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"emb_size = 128\n",
"node2vec = Node2Vec(emb_size, generator=generator)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"x_inp, x_out = node2vec.in_out_tensors()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the link_classification function to generate the prediction, with the 'dot' edge embedding generation method and the 'sigmoid' activation, which actually performs the dot product of the ``input embedding`` of the target node and the ``output embedding`` of the context node followed by a sigmoid activation. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"link_classification: using 'dot' method to combine node embeddings into edge embeddings\n"
]
}
],
"source": [
"prediction = link_classification(\n",
" output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"dot\"\n",
")(x_out)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stack the Node2Vec encoder and prediction layer into a Keras model. Our generator will produce batches of positive and negative context pairs as inputs to the model. Minimizing the binary crossentropy between the outputs and the provided ground truth is much like a regular binary classification task."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"model = keras.Model(inputs=x_inp, outputs=prediction)\n",
"\n",
"model.compile(\n",
" optimizer=keras.optimizers.Adam(lr=1e-3),\n",
" loss=keras.losses.binary_crossentropy,\n",
" metrics=[keras.metrics.binary_accuracy],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train the model."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train for 39760 steps\n",
"Epoch 1/2\n",
"39760/39760 [==============================] - 157s 4ms/step - loss: 0.2933 - binary_accuracy: 0.8553\n",
"Epoch 2/2\n",
"39760/39760 [==============================] - 201s 5ms/step - loss: 0.1082 - binary_accuracy: 0.9645\n"
]
}
],
"source": [
"history = model.fit(\n",
" generator.flow(unsupervised_samples),\n",
" epochs=epochs,\n",
" verbose=1,\n",
" use_multiprocessing=False,\n",
" workers=4,\n",
" shuffle=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualise Node Embeddings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build the node based model for predicting node representations from node ids and the learned parameters. Below a Keras model is constructed, with x_inp[0] as input and x_out[0] as output. Note that this model's weights are the same as those of the corresponding node encoder in the previously trained node pair classifier."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"x_inp_src = x_inp[0]\n",
"x_out_src = x_out[0]\n",
"embedding_model = keras.Model(inputs=x_inp_src, outputs=x_out_src)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the node embeddings from node ids."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"50/50 [==============================] - 0s 1ms/step\n"
]
}
],
"source": [
"node_gen = Node2VecNodeGenerator(G, batch_size).flow(G.nodes())\n",
"node_embeddings = embedding_model.predict(node_gen, workers=4, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform the embeddings to 2d space for visualisation."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"transform = TSNE # PCA\n",
"\n",
"trans = transform(n_components=2)\n",
"node_embeddings_2d = trans.fit_transform(node_embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# draw the embedding points, coloring them by the target label (paper subject)\n",
"alpha = 0.7\n",
"label_map = {l: i for i, l in enumerate(np.unique(subjects))}\n",
"node_colours = [label_map[target] for target in subjects]\n",
"\n",
"plt.figure(figsize=(7, 7))\n",
"plt.axes().set(aspect=\"equal\")\n",
"plt.scatter(\n",
" node_embeddings_2d[:, 0],\n",
" node_embeddings_2d[:, 1],\n",
" c=node_colours,\n",
" cmap=\"jet\",\n",
" alpha=alpha,\n",
")\n",
"plt.title(\"{} visualization of node embeddings\".format(transform.__name__))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Downstream task\n",
"\n",
"The node embeddings calculated using Node2Vec can be used as feature vectors in a downstream task such as node attribute inference (e.g., inferring the subject of a paper in Cora), community detection (clustering of nodes based on the similarity of their embedding vectors), and link prediction (e.g., prediction of citation links between papers)."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbsphinx": "hidden",
"tags": [
"CloudRunner"
]
},
"source": [
"