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Using the Laplacian of a graph to predict node labels.
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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Laplacian Eigenmaps\n",
"\n",
"\n",
"This article is an application of the article [\"Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering by Belkin and Niyogi.\"](https://www.semanticscholar.org/paper/Laplacian-Eigenmaps-and-Spectral-Techniques-for-and-Belkin-Niyogi/9d16c547d15a08091e68c86a99731b14366e3f0d) \n",
"\n",
"Graphs can be represented via their adjacency matrix and from there on one can use the well-developed field of algebraic graph theory. We show in simple steps how this representation can be used to perform node attribute inference on the Cora citation network. \n"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.manifold import TSNE\n",
"from sklearn.decomposition import PCA\n",
"import os\n",
"import networkx as nx\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.linear_model import LogisticRegressionCV\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import f1_score\n",
"\n",
"%matplotlib inline"
],
"outputs": [],
"execution_count": 3,
"metadata": {
"collapsed": true,
"tags": [],
"inputHidden": false
}
},
{
"cell_type": "markdown",
"source": [
"## Dataset\n",
"\n",
"The Cora dataset is the hello-world dataset when looking at graph learning. We have described in details in [this article](https://graphsandnetworks.com/the-cora-dataset/) and will not repeat it here. You can also find in the article a direct link to download the data.\n",
"\n",
"The construction below recreates the steps outlined in the article."
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"data_dir = os.path.expanduser(\"/Users/swa/Desktop/LargeFiles/Graphs/cora\")\n",
"cora_location = os.path.expanduser(os.path.join(data_dir, \"cora.cites\"))\n",
"g_nx = nx.read_edgelist(path=cora_location)\n",
"\n",
"\n",
"cora_data_location = os.path.expanduser(os.path.join(data_dir, \"cora.content\"))\n",
"node_attr = pd.read_csv(cora_data_location, sep='\\t', header=None)\n",
"values = { str(row.tolist()[0]): row.tolist()[-1] for _, row in node_attr.iterrows()}\n",
"nx.set_node_attributes(g_nx, values, 'subject')\n",
"\n",
"feature_names = [\"w_{}\".format(ii) for ii in range(1433)]\n",
"column_names = feature_names + [\"subject\"]\n",
"node_data = pd.read_table(os.path.join(data_dir, \"cora.content\"), header=None, names=column_names)\n",
"\n",
"g_nx_ccs = (g_nx.subgraph(c).copy() for c in nx.connected_components(g_nx))\n",
"g_nx = max(g_nx_ccs, key=len)\n",
"node_ids = list(g_nx.nodes())\n",
"print(\"Largest subgraph statistics: {} nodes, {} edges\".format(\n",
" g_nx.number_of_nodes(), g_nx.number_of_edges()))\n",
"\n",
"node_targets = [ g_nx.node[node_id]['subject'] for node_id in node_ids]\n",
"\n",
"print(f\"There are {len(np.unique(node_targets))} unique labels on the nodes.\")\n",
"\n",
"print(f\"There are {len(g_nx.nodes())} nodes in the network.\")"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/Users/swa/conda/lib/python3.7/site-packages/ipykernel_launcher.py:13: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n",
" del sys.path[0]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Largest subgraph statistics: 2485 nodes, 5069 edges\n",
"There are 7 unique labels on the nodes.\n",
"There are 2485 nodes in the network.\n"
]
}
],
"execution_count": 8,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"We map the subject to a color for rendering purposes."
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"colors = {'Case_Based': 'black',\n",
" 'Genetic_Algorithms': 'red',\n",
" 'Neural_Networks': 'blue',\n",
" 'Probabilistic_Methods': 'green',\n",
" 'Reinforcement_Learning': 'aqua',\n",
" 'Rule_Learning': 'purple',\n",
" 'Theory': 'yellow'}"
],
"outputs": [],
"execution_count": 9,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"## The graph Laplacian"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"There are at leat 3 graph Laplacians in use. These are called [unormalized, random walk and normalised graph Laplacian](https://en.wikipedia.org/wiki/Laplacian_matrix) and they are defined as follows:\n",
"\n",
"- Unormalized: $L = D-A$\n",
"- Random Walk: $L_{rw} = D^{-1}L = I - D^{-1}A$\n",
"- Normalised: $L_{sym} = D^{-1/2}LD^{-1/2} = I - D^{-1/2}AD^{-1/2}$\n",
"\n",
"We'll use the unormalised graph Laplacian from here on."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"The adjacency matrix of the graph in numpy format:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"A = nx.to_numpy_array(g_nx) "
],
"outputs": [],
"execution_count": 10,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"and the degree matrix from this:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"D = np.diag(A.sum(axis=1))\n",
"\n",
"print(D)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[168. 0. 0. ... 0. 0. 0.]\n",
" [ 0. 5. 0. ... 0. 0. 0.]\n",
" [ 0. 0. 6. ... 0. 0. 0.]\n",
" ...\n",
" [ 0. 0. 0. ... 4. 0. 0.]\n",
" [ 0. 0. 0. ... 0. 4. 0.]\n",
" [ 0. 0. 0. ... 0. 0. 2.]]\n"
]
}
],
"execution_count": 11,
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"So, the unnormalized Laplacian is"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"L = D-A\n",
"print(L)"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[[168. -1. -1. ... 0. 0. 0.]\n",
" [ -1. 5. 0. ... 0. 0. 0.]\n",
" [ -1. 0. 6. ... 0. 0. 0.]\n",
" ...\n",
" [ 0. 0. 0. ... 4. -1. -1.]\n",
" [ 0. 0. 0. ... -1. 4. 0.]\n",
" [ 0. 0. 0. ... -1. 0. 2.]]\n"
]
}
],
"execution_count": 14,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"## Eigenvectors and eigenvalues of the Laplacian\n",
"\n",
"Numpy can directly give you all you need in one line:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"eigenvalues, eigenvectors = np.linalg.eig(L) "
],
"outputs": [],
"execution_count": 23,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"In general, eigenvalues can be complex. Only special types of matrices give rise to real values only. So, we'll take the real parts only and assume that the dropped complex dimension does not contain significant information."
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"eigenvalues = np.real(eigenvalues)\n",
"eigenvectors = np.real(eigenvectors)"
],
"outputs": [],
"execution_count": 24,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"Let's also order the eigenvalues from small to large:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"order = np.argsort(eigenvalues) \n",
"eigenvalues = eigenvalues[order]"
],
"outputs": [],
"execution_count": 25,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": false
}
},
{
"cell_type": "markdown",
"source": [
"For example, the first eigenvalues are:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"eigenvalues[0:10]"
],
"outputs": [
{
"output_type": "execute_result",
"execution_count": 28,
"data": {
"text/plain": [
"array([3.33303173e-15, 1.48014820e-02, 2.36128446e-02, 3.03008575e-02,\n",
" 4.06458495e-02, 4.72354991e-02, 5.65503673e-02, 6.00350936e-02,\n",
" 7.24399539e-02, 7.45956530e-02])"
]
},
"metadata": {}
}
],
"execution_count": 28,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": false
}
},
{
"cell_type": "markdown",
"source": [
"The first eigenvalue is as good as zero and this is a general fact; the smallest eigenvalue is always zero. The reason it's not exactly zero above is because of computational accuracy.\n",
"\n",
"So, we will omit the first eigenvector since it does not convey any information.\n",
"\n",
"We also take a 32-dimensional subspace of the full vector space:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"embedding_size = 32\n",
"v_0 = eigenvectors[:, order[0]]\n",
"v = eigenvectors[:, order[1:(embedding_size+1)]] "
],
"outputs": [],
"execution_count": 30,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "markdown",
"source": [
"A plot of the eigenvalue looks like the following:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"plt.plot(eigenvalues)"
],
"outputs": [
{
"output_type": "execute_result",
"execution_count": 31,
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x122043ac8>]"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": [
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\n"
]
},
"metadata": {
"needs_background": "light"
}
}
],
"execution_count": 31,
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Let's use t-SNE to visualize out 32-dimensional organization:"
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"tsne = TSNE() \n",
"v_pr = tsne.fit_transform(v)"
],
"outputs": [],
"execution_count": 34,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "code",
"source": [
"alpha=0.7\n",
"label_map = { l: i for i, l in enumerate(np.unique(node_targets))}\n",
"node_colours = [ label_map[target] for target in node_targets]\n",
"\n",
"fig = plt.figure(figsize=(10,8))\n",
"plt.scatter(v_pr[:,0], \n",
" v_pr[:,1], \n",
" c=node_colours, cmap=\"jet\", alpha=alpha)\n"
],
"outputs": [
{
"output_type": "execute_result",
"execution_count": 35,
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x122205e80>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
],
"image/png": [
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\n"
]
},
"metadata": {
"needs_background": "light"
}
}
],
"execution_count": 35,
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"We see that the eigenvectors of the Laplacian form clusters corresponding to the target labels. It means that in principle we can train a model using the eigenvectors and make predictions about an unseen graphs. Simply said, given an unlabelled graph we can extract its Laplacian, feed it to the model and get labels for the nodes."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## Training a classifier\n",
"\n",
"The eigenvectors are from the point of view of machine learning just ordinary feature vectors. Taking a training and test set really means in this case taking a subset of the nodes (a subgraph) even though on a code level it's just an ordinary classifier."
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"X = v\n",
"Y = np.array(node_targets)"
],
"outputs": [],
"execution_count": 37,
"metadata": {
"collapsed": true
}
},
{
"cell_type": "code",
"source": [
"clf = RandomForestClassifier(n_estimators=10, min_samples_leaf=4)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, Y, train_size=140, random_state=42)"
],
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/Users/swa/conda/lib/python3.7/site-packages/sklearn/model_selection/_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
" FutureWarning)\n"
]
}
],
"execution_count": 38,
"metadata": {}
},
{
"cell_type": "code",
"source": [
"clf.fit(X_train, y_train)"
],
"outputs": [
{
"output_type": "execute_result",
"execution_count": 39,
"data": {
"text/plain": [
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=4, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
" oob_score=False, random_state=None, verbose=0,\n",
" warm_start=False)"
]
},
"metadata": {}
}
],
"execution_count": 39,
"metadata": {}
},
{
"cell_type": "code",
"source": [
"print(\"score on X_train {}\".format(clf.score(X_train, y_train)))\n",
"print(\"score on X_test {}\".format(clf.score(X_test, y_test)))"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"score on X_train 0.8571428571428571\n",
"score on X_test 0.7292110874200426\n"
]
}
],
"execution_count": 40,
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"From here on you can experiment with different embedding dimensions, different Laplacians or any other matrix representing the structure of the graph."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3"
},
"language_info": {
"name": "python",
"version": "3.7.2",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernel_info": {
"name": "python3"
},
"nteract": {
"version": "0.15.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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