demo.ipynb

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 "metadata": {
  "language_info": {
   "name": "python",
   "codemirror_mode": {
    "name": "ipython",
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   "version": "3.7.3"
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 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# node2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from node2vec.model import Node2Vec\n",
    "\n",
    "# Create an edgelist\n",
    "src_nodes = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8], dtype=np.int32)\n",
    "dest_nodes = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0], dtype=np.int32)\n",
    "\n",
    "# Create a node2vec model from the edgelist\n",
    "node2vec_model = Node2Vec(src_nodes, dest_nodes, graph_is_directed=False)\n",
    "\n",
    "# Simulate biased random walks on the graph\n",
    "node2vec_model.simulate_walks(\n",
    "    walk_length=25,\n",
    "    n_walks=10,\n",
    "    p=1,\n",
    "    q=0.5,\n",
    "    workers=2\n",
    ")\n",
    "\n",
    "# Print the generated walks\n",
    "print(node2vec_model.walks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Learn node embeddings from the generated random walks\n",
    "node2vec_model.learn_embeddings(\n",
    "    dimensions=64,\n",
    "    context_size=10,\n",
    "    epochs=2,\n",
    "    workers=2\n",
    ")\n",
    "\n",
    "# Print the embedding corresponding to the first node\n",
    "print(node2vec_model.embeddings[0])"
   ]
  }
 ]
}