{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "Pxkh0PMuE5i2" }, "outputs": [], "source": [ "import sys\n", "import networkx as nx\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "print(\"Python version:\", sys.version)\n", "print(\"NetworkX version:\", nx.__version__)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mM9k_ZXnE5i4" }, "outputs": [], "source": [ "a = 2\n", "b = 3" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vB19bk63E5i5" }, "outputs": [], "source": [ "print(a)\n", "print(b)" ] }, { "cell_type": "code", "source": [ "b = 7" ], "metadata": { "id": "kU1ncctR2b9q" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Jh2emYcPE5i5" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "F-JrvYNnE5i5" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "\n", "plt.rcParams['figure.figsize'] = (16,10)\n", "\n", "G = nx.karate_club_graph()\n", "\n", "for n in G.nodes():\n", " print(n, G.degree(n))\n", "\n", "nx.draw(G, with_labels = True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "I9wciHD8E5i5" }, "outputs": [], "source": [ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import random\n", "\n", "G=nx.Graph()\n", "G.add_node(\"Student3\")\n", "G.add_edge(\"Student1\", \"Student2\")\n", "G.add_edge(\"Student3\", \"Student4\")\n", "G.add_edge(\"Student2\", \"Student4\")\n", "\n", "\n", "print(\"Edges list:\")\n", "for edge in G.edges():\n", " print(edge)\n", "print()\n", "\n", "print(\"Adjacency list\")\n", "for node in G.nodes():\n", " print(node, G[node])\n", "print()\n", "\n", "print(\"Adjacency matrix\")\n", "A = nx.adjacency_matrix(G)\n", "print(A.todense())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rfptIAM7E5i6" }, "outputs": [], "source": [ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import random\n", "\n", "G=nx.Graph()\n", "p = 0.6\n", "people = {\"Alice\", \"Bob\", \"Chuck\", \"Dima\", \"Eve\"}\n", "\n", "color_map = []\n", "\n", "for p1 in people:\n", " G.add_node(p1)\n", " for p2 in people:\n", " if p1 > p2:\n", " coin = random.uniform(0, 1)\n", " if coin <= p:\n", " G.add_edge(p1, p2)\n", "\n", "for node in G.nodes():\n", " if G.degree(node) >= 2:\n", " color_map.append(\"red\")\n", " elif G.degree(node) >= 1:\n", " color_map.append(\"orange\")\n", " else:\n", " color_map.append(\"yellow\")\n", "\n", "\n", "nx.draw(G, node_color = color_map, with_labels = True, node_size=1200)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JB7Z4C91E5i7" }, "outputs": [], "source": [ "import networkx as nx\n", "\n", "G=nx.Graph()\n", "G.add_nodes_from([2,3,4,5])\n", "\n", "nx.draw(G, with_labels = True, node_size=1000)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b2wvKRmIE5i7" }, "outputs": [], "source": [ "import networkx as nx\n", "\n", "G = nx.Graph()\n", "G.add_edges_from([(1,2),\n", " (1,3),\n", " (3,4),\n", " (1,5),\n", " (3,5),\n", " (4,2),\n", " (2,3),\n", " (1,7),\n", " (7,8),\n", " (3,6)])\n", "\n", "\n", "nx.draw(G, with_labels=True, font_weight='bold')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GVjFkBWpE5i7" }, "outputs": [], "source": [ "import collections\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "\n", "degree_sequence = sorted([d for n, d in G.degree()], reverse=True)\n", "degreeCount = collections.Counter(degree_sequence)\n", "deg, cnt = zip(*degreeCount.items())\n", "\n", "fig, ax = plt.subplots()\n", "plt.bar(deg, cnt, width=0.60, color='g')\n", "plt.title(\"Degree Histogram\")\n", "plt.ylabel(\"Count\")\n", "plt.xlabel(\"Degree\")\n", "\n", "ax.set_xticks(deg)\n", "ax.set_xticklabels(deg)\n", "\n", "# draw graph in inset\n", "plt.axes([0.4, 0.4, 0.5, 0.5])\n", "pos = nx.spring_layout(G)\n", "plt.axis('off')\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2Il8YCNkE5i8" }, "outputs": [], "source": [ "CC = nx.clustering(G)\n", "for node in CC:\n", " print(node, \"\\t\", CC[node])\n", "\n", "nx.draw(G, with_labels=True, font_weight='bold')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "d_76yq6KE5i8" }, "outputs": [], "source": [ "import networkx as nx\n", "\n", "G = nx.Graph()\n", "G.add_edge(1,2,color='r',weight=5)\n", "G.add_edge(2,3,color='b',weight=10)\n", "G.add_edge(3,4,color='g',weight=15)\n", "\n", "pos = nx.circular_layout(G)\n", "\n", "edges = G.edges()\n", "colors = [G[u][v]['color'] for u,v in edges]\n", "weights = [G[u][v]['weight'] for u,v in edges]\n", "\n", "nx.draw(G, pos, with_labels=True, edge_color=colors, width=weights)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BGEzmW4jE5i8" }, "outputs": [], "source": [ "import networkx as nx\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "\n", "G = nx.DiGraph()\n", "\n", "G.add_edges_from([('A','B'),('C','D'),('G','D')], weight=1)\n", "G.add_edges_from([('D','A'),('D','E'),('B','D'),('D','E')], weight=2)\n", "G.add_edges_from([('B','C'),('E','F')], weight=3)\n", "G.add_edges_from([('C','F')], weight=4)\n", "\n", "\n", "\n", "edge_labels = dict([((u,v,),d['weight']) for u,v,d in G.edges(data=True)])\n", "special_edges = [('C','D'),('D','A')]\n", "edge_colors = ['black' if not edge in special_edges else 'red' for edge in G.edges()]\n", "\n", "pos=nx.kamada_kawai_layout(G)\n", "nx.draw_networkx_edge_labels(G,pos,edge_labels=edge_labels)\n", "nx.draw(G, pos, with_labels=True, font_weight='bold', node_size=450, edge_color=edge_colors)\n", "pylab.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fmcD6eVNE5i9" }, "outputs": [], "source": [ "# Author: Aric Hagberg (hagberg@lanl.gov)\n", "\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "\n", "G = nx.star_graph(20)\n", "pos = nx.spring_layout(G)\n", "colors = range(20)\n", "nx.draw(G, pos, node_color='#A0CBE2', edge_color=colors,\n", " width=4, edge_cmap=plt.cm.Blues, with_labels=False)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "id": "LKlOwfRLE5i9" }, "outputs": [], "source": [ "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" }, "colab": { "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }