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1014 lines
45 KiB
Plaintext
1014 lines
45 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"![](images/EscUpmPolit_p.gif \"UPM\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"# Course Notes for Learning Intelligent Systems"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"## [Introduction to Network Analysis](0_Intro_Network_Analysis.ipynb)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "skip"
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}
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},
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"source": [
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"# Table of Contents\n",
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"\n",
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"* [Working with Graphs](#Working-with-Graphs)\n",
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"* [First steps](#First-steps)\n",
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"* [Reading Data from a File](#Reading-Data-from-a-File)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"# Working with Graphs\n",
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"\n",
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"\n",
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"## Import networkx\n",
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"The first thing is importing the package.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"import networkx as nx"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Graph"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"G = nx.Graph()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Nodes\n",
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"NetworkX is very flexible. Nodes can be any hashable object, such as strings, numbers, files, functions, etc."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"G.add_node(1) # integer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"G.add_node('A') #string"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"import math\n",
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"G.add_node(math.cos) #cosine function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 'A', <built-in function cos>]\n"
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]
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}
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],
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"source": [
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"print(G.nodes)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"## Edges\n",
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"Edges (links) are represented as tuples of nodes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"G.add_edge(1, 'A')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"G.add_edge('B', math.cos)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[(1, 'A'), (<built-in function cos>, 'B')]\n"
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]
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}
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],
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"source": [
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"print(G.edges)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"slideshow": {
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"slide_type": "subslide"
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}
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},
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"source": [
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"## Edge data\n",
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"\n",
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"Edge data is assigned using a tuple (node1, node2, data). \n",
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"\n",
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"Default data is {} (empty dictionary), but any Python object is allowed."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
|
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"slideshow": {
|
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"slide_type": "fragment"
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}
|
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},
|
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"outputs": [],
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"source": [
|
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"W = nx.Graph()"
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]
|
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},
|
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{
|
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
|
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"slideshow": {
|
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"slide_type": "fragment"
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}
|
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},
|
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"outputs": [],
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"source": [
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"W.add_edge('A', 'B', weight = 1)"
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]
|
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},
|
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{
|
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
|
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"slideshow": {
|
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"W.add_weighted_edges_from([('A', 'C', 3), ('A', 'D', 4), ('B', 'D', 2)])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
|
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"slideshow": {
|
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"slide_type": "subslide"
|
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}
|
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},
|
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"outputs": [
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{
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"data": {
|
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"text/plain": [
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"{('A', 'B'): Text(0.34360816042257025, 0.03772295893763749, '1'),\n",
|
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" ('A', 'C'): Text(-0.6125262066395303, 0.16919086402801908, '3'),\n",
|
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" ('A', 'D'): Text(0.04386563293789962, -0.17119492412470283, '4'),\n",
|
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" ('B', 'D'): Text(0.6125262066395304, -0.16919086402801906, '2')}"
|
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]
|
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},
|
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"execution_count": 14,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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},
|
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{
|
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"pos = nx.spring_layout(W) # positions for all nodes\n",
|
|
"labels = nx.get_edge_attributes(W,'weight')\n",
|
|
"nx.draw_networkx(W, pos=pos, node_size=800, node_color='y', font_size=20)\n",
|
|
"nx.draw_networkx_edges(W,pos,width=4, edge_color='g', arrows=False)\n",
|
|
"nx.draw_networkx_edge_labels(W,pos,edge_labels=labels, font_color='brown', font_size=15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"source": [
|
|
"For more info about draw_networkx see the [NetworkX manual](https://networkx.github.io/documentation/networkx-1.10/reference/generated/networkx.drawing.nx_pylab.draw_networkx.html#networkx.drawing.nx_pylab.draw_networkx)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"source": [
|
|
"Let's access edges"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[('A', 'B'), ('A', 'C'), ('A', 'D'), ('B', 'D')]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(W.edges) # List of all edges; same than W.edges() with default parameters"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[('A', 'B', {'weight': 1}), ('A', 'C', {'weight': 3}), ('A', 'D', {'weight': 4}), ('B', 'D', {'weight': 2})]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(W.edges(data=True)) # List of all edges with data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[('A', 'B', 1), ('A', 'C', 3), ('A', 'D', 4), ('B', 'D', 2)]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(W.edges(data='weight')) # List of all edges with attribute 'weight'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"source": [
|
|
"Let's calculate Dijsktra shortest weighted path:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{('A', 'B'): Text(-0.17186208433735098, -0.2876883893655454, '1'),\n",
|
|
" ('A', 'C'): Text(0.10060091197009038, 0.6027184602573036, '3'),\n",
|
|
" ('A', 'D'): Text(0.1315138115456491, -0.10959315037715107, '4'),\n",
|
|
" ('B', 'D'): Text(-0.10060091197009048, -0.6027184602573037, '2')}"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"pos = nx.spring_layout(W) # positions for all nodes\n",
|
|
"labels = nx.get_edge_attributes(W,'weight')\n",
|
|
"nx.draw_networkx(W, pos=pos, node_color='red', node_size=600)\n",
|
|
"nx.draw_networkx_edges(W,pos,width=4, edge_color='yellow', arrows=False)\n",
|
|
"nx.draw_networkx_edge_labels(W,pos,edge_labels=labels, font_color='brown', font_size=15)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['C', 'A', 'B', 'D']"
|
|
]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"nx.dijkstra_path(W, 'C', 'D')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "slide"
|
|
}
|
|
},
|
|
"source": [
|
|
"## NetworkX design principles\n",
|
|
"* \"Node centric\" view of networks\n",
|
|
"* Nodes: any hashable object\n",
|
|
"* Edges: three tuples (n1, n2, d) with optional edge data\n",
|
|
"* Edge data can be defined by users"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Network data structure\n",
|
|
"* Uses a \"dictionary of dictionaries\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"source": [
|
|
"* **G.adj** is the Graph adjacency object: \n",
|
|
" * **key**: node; \n",
|
|
" * **value**: neighbor-dicts"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{'A': {'B': {'weight': 1}, 'C': {'weight': 3}, 'D': {'weight': 4}}, 'B': {'A': {'weight': 1}, 'D': {'weight': 2}}, 'C': {'A': {'weight': 3}}, 'D': {'A': {'weight': 4}, 'B': {'weight': 2}}}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(W.adj)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "slide"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Simple operations\n",
|
|
"In this way, it is easy..."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"True"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Test if a node is in the graph\n",
|
|
"'A' in W"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"A\n",
|
|
"B\n",
|
|
"C\n",
|
|
"D\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# loop over all the nodes\n",
|
|
"for n in W:\n",
|
|
" print(n)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "slide"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Edge attributes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'weight': 1}"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Access an edge\n",
|
|
"\n",
|
|
"W['A']['B']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'weight': 1}"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"W.adj['A']['B']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"1"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Access an attribute\n",
|
|
"W['A']['B']['weight']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Multigraphs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"0"
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"MG = nx.MultiGraph()\n",
|
|
"MG.add_edge('A', 'B', relation = 'friend', color = 'b', weight = 3)\n",
|
|
"MG.add_edge('A', 'B', relation = 'neighbour', color = 'r', weight = 8)\n",
|
|
"MG.add_edge('A', 'C', relation = 'father', color = 'c', weight = 1)\n",
|
|
"MG.add_edge('A', 'D', relation = 'friend', color = 'b', weight = 2)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{0: {'relation': 'friend', 'color': 'b', 'weight': 3}, 1: {'relation': 'neighbour', 'color': 'r', 'weight': 8}}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#Dictionary per every edge\n",
|
|
"print(MG['A']['B'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "slide"
|
|
}
|
|
},
|
|
"source": [
|
|
"# Node attributes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"G = nx.Graph()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"G.add_edge('Predeal', 'Bucharest', distance=158)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"G.add_edge('Predeal', 'Brasov', distance=26.7)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"G.add_node('Predeal', population=4755)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"G.add_node('Bucharest', population=1812290)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"G.add_node('Brasov', population=550547)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "subslide"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['Predeal', 'Bucharest', 'Brasov']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# List of nodes \n",
|
|
"print(G.nodes())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[('Predeal', {'population': 4755}), ('Bucharest', {'population': 1812290}), ('Brasov', {'population': 550547})]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#list of nodes attributes\n",
|
|
"print(G.nodes(data=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "fragment"
|
|
}
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"4755\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"#Access one attribute\n",
|
|
"print(G.nodes['Predeal']['population'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "skip"
|
|
}
|
|
},
|
|
"source": [
|
|
"# References\n",
|
|
"* Networkx tutorial https://networkx.github.io/documentation/stable/tutorial.html"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "skip"
|
|
}
|
|
},
|
|
"source": [
|
|
"## Licence"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"slideshow": {
|
|
"slide_type": "skip"
|
|
}
|
|
},
|
|
"source": [
|
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
|
"\n",
|
|
"© Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Slideshow",
|
|
"datacleaner": {
|
|
"position": {
|
|
"top": "50px"
|
|
},
|
|
"python": {
|
|
"varRefreshCmd": "try:\n print(_datacleaner.dataframe_metadata())\nexcept:\n print([])"
|
|
},
|
|
"window_display": false
|
|
},
|
|
"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": "3.11.7"
|
|
},
|
|
"latex_envs": {
|
|
"LaTeX_envs_menu_present": true,
|
|
"autocomplete": true,
|
|
"bibliofile": "biblio.bib",
|
|
"cite_by": "apalike",
|
|
"current_citInitial": 1,
|
|
"eqLabelWithNumbers": true,
|
|
"eqNumInitial": 1,
|
|
"hotkeys": {
|
|
"equation": "Ctrl-E",
|
|
"itemize": "Ctrl-I"
|
|
},
|
|
"labels_anchors": false,
|
|
"latex_user_defs": false,
|
|
"report_style_numbering": false,
|
|
"user_envs_cfg": false
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|