{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Toy graphs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook shows how to load some toy graphs."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"pycharm": {
"is_executing": false
},
"scrolled": true
},
"outputs": [],
"source": [
"from IPython.display import SVG"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"from sknetwork.data import house, bow_tie, karate_club, miserables, painters, hourglass, star_wars, movie_actor\n",
"from sknetwork.visualization import visualize_graph, visualize_bigraph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## House graph"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"graph = house(metadata=True)\n",
"adjacency = graph.adjacency\n",
"position = graph.position"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"image = visualize_graph(adjacency, position, scale=0.5)\n",
"SVG(image)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# adjacency matrix only\n",
"adjacency = house()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bow tie"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"graph = bow_tie(metadata=True)\n",
"adjacency = graph.adjacency\n",
"position = graph.position"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"image = visualize_graph(adjacency, position, scale=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SVG(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Karate club"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"graph = karate_club(metadata=True)\n",
"adjacency = graph.adjacency\n",
"position = graph.position\n",
"labels = graph.labels"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"image = visualize_graph(adjacency, position, labels=labels)\n",
"SVG(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Les Miserables"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"graph = miserables(metadata=True)\n",
"adjacency = graph.adjacency\n",
"position = graph.position\n",
"names = graph.names"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"image = visualize_graph(adjacency, position, names, scale=2)\n",
"SVG(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Painters"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"graph = painters(metadata=True)\n",
"adjacency = graph.adjacency\n",
"names = graph.names\n",
"position = graph.position"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"image = visualize_graph(adjacency, position, names)\n",
"SVG(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Star wars"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"# bipartite graph\n",
"graph = star_wars(metadata=True)\n",
"biadjacency = graph.biadjacency\n",
"names_row = graph.names_row\n",
"names_col = graph.names_col"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"image = visualize_bigraph(biadjacency, names_row, names_col)\n",
"SVG(image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# biadjacency matrix only\n",
"biadjacency = star_wars()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Movie-actor"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"# bipartite graph\n",
"graph = movie_actor(metadata=True)\n",
"biadjacency = graph.biadjacency\n",
"names_row = graph.names_row\n",
"names_col = graph.names_col"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"image = visualize_bigraph(biadjacency, names_row, names_col)\n",
"SVG(image)"
]
}
],
"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": "3.9.13"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": []
}
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
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"nbformat": 4,
"nbformat_minor": 2
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