"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the python implementation of how to perform node classification with the attri2vec algorithm outlined in paper [1]. The implementation uses the stellargraph components.\n",
"\n",
"\n",
"**References:** \n",
"\n",
"[1] [Attributed Network Embedding via Subspace Discovery](https://link.springer.com/article/10.1007/s10618-019-00650-2). D. Zhang, Y. Jie, X. Zhu and C. Zhang, Data Mining and Knowledge Discovery, 2019. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## attri2vec\n",
"\n",
"attri2vec learns node representations by performing a linear/non-linear mapping on node content attributes. To make the learned node representations respect structural similarity, [DeepWalk](https://dl.acm.org/citation.cfm?id=2623732)/[Node2Vec](https://snap.stanford.edu/node2vec) learning mechanism is used to make nodes sharing similar random walk context nodes represented closely in the subspace. \n",
"\n",
"For each (``target``,``context``) node pair $(v_i,v_j)$ collected from random walks, attri2vec learns 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 raw content feature vector in the input layer with the input-to-hidden weight matrix $W_{in}$ followed by an activation function. 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 [1]. 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 [1], 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": [
"The entire model is trained end-to-end by minimizing the binary cross-entropy loss function with regards to predicted node pair labels and true node pair labels, using stochastic gradient descent (SGD) updates of the model parameters, with minibatches of 'training' node pairs generated on demand and fed into the model."
]
},
{
"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 networkx as nx\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import random\n",
"\n",
"import stellargraph as sg\n",
"from stellargraph.data import UnsupervisedSampler\n",
"from stellargraph.mapper import Attri2VecLinkGenerator, Attri2VecNodeGenerator\n",
"from stellargraph.layer import Attri2Vec, link_classification\n",
"\n",
"from tensorflow import keras\n",
"\n",
"from pandas.core.indexes.base import Index\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.manifold import TSNE\n",
"from sklearn.decomposition import PCA\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegressionCV\n",
"from sklearn.metrics import accuracy_score\n",
"from stellargraph import datasets\n",
"from IPython.display import display, HTML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
"\n",
"For clarity we ignore isolated nodes and subgraphs and use only the largest connected component."
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"DataLoadingLinks"
]
},
"source": [
"(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"tags": [
"DataLoading"
]
},
"outputs": [
{
"data": {
"text/html": [
"The CiteSeer dataset consists of 3312 scientific publications classified into one of six classes. The citation network consists of 4732 links, although 17 of these have a source or target publication that isn't in the dataset and only 4715 are included in the graph. 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 3703 unique words."
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dataset = datasets.CiteSeer()\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: 2110, Edges: 3757\n",
"\n",
" Node types:\n",
" paper: [2110]\n",
" Features: float32 vector, length 3703\n",
" Edge types: paper-cites->paper\n",
"\n",
" Edge types:\n",
" paper-cites->paper: [3757]\n"
]
}
],
"source": [
"print(G.info())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train attri2vec on Citeseer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specify the other optional parameter values: root nodes, the number of walks to take per node, the length of each walk."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"nodes = list(G.nodes())\n",
"number_of_walks = 4\n",
"length = 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the UnsupervisedSampler instance with the relevant parameters passed to it."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"unsupervised_samples = UnsupervisedSampler(\n",
" G, nodes=nodes, length=length, number_of_walks=number_of_walks\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the batch size and the number of epochs. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 50\n",
"epochs = 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define an attri2vec generator, which generates batches of (target, context) nodes and labels for the node pair."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"generator = Attri2VecLinkGenerator(G, batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Building the model: a 1-hidden-layer node representation ('input embedding') of the `target` node and the parameter vector ('output embedding') for predicting the existence of `context node` for each `(target context)` pair, with a link classification layer performed on the dot product of the 'input embedding' of the `target` node and the 'output embedding' of the `context` node.\n",
"\n",
"Attri2Vec part of the model, with a 128-dimenssion hidden layer, no bias term and no normalization. (Normalization can be set to 'l2'). "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"layer_sizes = [128]\n",
"attri2vec = Attri2Vec(\n",
" layer_sizes=layer_sizes, generator=generator, bias=False, normalize=None\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Build the model and expose input and output sockets of attri2vec, for node pair inputs:\n",
"x_inp, x_out = attri2vec.in_out_tensors()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the link_classification function to generate the prediction, with the 'ip' 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": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"link_classification: using 'ip' method to combine node embeddings into edge embeddings\n"
]
}
],
"source": [
"prediction = link_classification(\n",
" output_dim=1, output_act=\"sigmoid\", edge_embedding_method=\"ip\"\n",
")(x_out)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stack the Attri2Vec encoder and prediction layer into a Keras model, and specify the loss."
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" ['...']\n",
"Train for 1351 steps\n",
"Epoch 1/4\n",
"1351/1351 - 4s - loss: 0.6803 - binary_accuracy: 0.5623\n",
"Epoch 2/4\n",
"1351/1351 - 4s - loss: 0.5154 - binary_accuracy: 0.7565\n",
"Epoch 3/4\n",
"1351/1351 - 3s - loss: 0.3169 - binary_accuracy: 0.8939\n",
"Epoch 4/4\n",
"1351/1351 - 4s - loss: 0.2053 - binary_accuracy: 0.9450\n"
]
}
],
"source": [
"history = model.fit(\n",
" generator.flow(unsupervised_samples),\n",
" epochs=epochs,\n",
" verbose=2,\n",
" use_multiprocessing=False,\n",
" workers=1,\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 content attributes with 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": 15,
"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 by applying the learned mapping function to node content features."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"43/43 [==============================] - 0s 2ms/step\n"
]
}
],
"source": [
"node_gen = Attri2VecNodeGenerator(G, batch_size).flow(G.nodes())\n",
"node_embeddings = embedding_model.predict(node_gen, workers=1, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Transform the embeddings to 2d space for visualisation."
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"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": [
"## Node Classificaion Task"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The embeddings learned by `attri2vec` can be used as feature vectors in downstream tasks, such as node classification and link prediction.\n",
"\n",
"In this example, we will use the `attri2vec` node embeddings to train a classifier to predict the subject of a paper in DBLP."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# X will hold the 128-dimensional input features\n",
"X = node_embeddings\n",
"# y holds the corresponding target values\n",
"y = np.array(subjects)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Splitting\n",
"\n",
"We split the data into train and test sets. \n",
"\n",
"We use 20% of the data for training and the remaining 80% for testing as a hold out test set."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Array shapes:\n",
" X_train = (422, 128)\n",
" y_train = (422,)\n",
" X_test = (1688, 128)\n",
" y_test = (1688,)\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.2, test_size=None)\n",
"print(\n",
" \"Array shapes:\\n X_train = {}\\n y_train = {}\\n X_test = {}\\n y_test = {}\".format(\n",
" X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Classifier Training\n",
"\n",
"We train a Logistic Regression classifier on the training data. "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegressionCV(Cs=10, class_weight=None, cv=10, dual=False,\n",
" fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
" max_iter=1000, multi_class='ovr', n_jobs=None,\n",
" penalty='l2', random_state=None, refit=True,\n",
" scoring='accuracy', solver='lbfgs', tol=0.0001,\n",
" verbose=False)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = LogisticRegressionCV(\n",
" Cs=10, cv=10, scoring=\"accuracy\", verbose=False, multi_class=\"ovr\", max_iter=1000\n",
")\n",
"clf.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predict the hold-out test set."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"y_pred = clf.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate the accuracy of the classifier on the test set."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.754739336492891"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_score(y_test, y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbsphinx": "hidden",
"tags": [
"CloudRunner"
]
},
"source": [
"