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mikk-c/exercisev2_42_5.ipynb

Created August 6, 2024 11:39
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exercisev2_42_5.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d6d6b476-e6ba-4593-83db-6b62a44ea34d",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import networkx as nx\n",
"from numpy.linalg import svd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4b8ccb89-e1cb-428e-83c7-fd4fc9c8179e",
"metadata": {},
"outputs": [],
"source": [
"H = nx.read_edgelist(\"1/data.txt\", delimiter = \"\\t\", nodetype = int)\n",
"G = nx.Graph()\n",
"G.add_nodes_from(sorted(H.nodes))\n",
"G.add_edges_from(H.edges)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "16afde7d-2793-45c8-9664-5d207000fb9a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.26690348, -0.28863056],\n",
" [-0.33362935, -0.40241527],\n",
" [-0.19064534, -0.13317155],\n",
" [-0.22241957, -0.13901232],\n",
" [-0.19064534, -0.14331661],\n",
" [-0.22241957, -0.1999696 ],\n",
" [-0.22241957, -0.22055087],\n",
" [-0.26690348, -0.15998305],\n",
" [-0.19064534, 0.13403319],\n",
" [-0.22241957, 0.13655563],\n",
" [-0.22241957, 0.16338103],\n",
" [-0.26690348, 0.2366512 ],\n",
" [-0.26690348, 0.2366512 ],\n",
" [-0.44483914, 0.61747469],\n",
" [-0.22241957, 0.16230293]])"
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},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D = np.array(list(dict(G.degree).values())) ** (1/2)\n",
"L = nx.laplacian_matrix(G).todense()\n",
"L = D * L * D\n",
"vals, vects = np.linalg.eig(L)\n",
"embeddings = vects[:,:2]\n",
"embeddings"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "df19cce6-9cd0-4fa1-96b4-84d2c1c27b4e",
"metadata": {},
"outputs": [],
"source": [
"blocks = list(nx.community.label_propagation_communities(G))\n",
"virtual_node_1_embedding = np.mean([embeddings[v - 1] for v in blocks[0]], axis = 0)\n",
"virtual_node_2_embedding = np.mean([embeddings[v - 1] for v in blocks[1]], axis = 0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6bf4b469-f847-4212-9f69-c6bc5408ce8c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-0.25093126, 0.01506295])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hierarchical_graph_embedding = np.mean([virtual_node_1_embedding, virtual_node_2_embedding], axis = 0)\n",
"hierarchical_graph_embedding"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "433151f5-8f11-4e0b-8a7b-9645cd6beaa2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([-2.50169057e-01, -3.75625457e-16])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flat_graph_embedding = np.mean(embeddings, axis = 0)\n",
"flat_graph_embedding"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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