Delete sna/t.txt

README.md

@@ -1,7 +1,7 @@
 # sitc
 Exercises for Intelligent Systems Course at Universidad Politécnica de Madrid, Telecommunication Engineering School. This material is used in the subjects
-- SITC (Sistemas Inteligentes y Tecnologías del Conocimiento) - Master Universitario de Ingeniería de Telecomunicación (MUIT)
-- TIAD (Tecnologías Inteligentes de Análisis de Datos) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos)
+- CDAW (Ciencia de datos y aprendizaje en automático en la web de datos) - Master Universitario de Ingeniería de Telecomunicación (MUIT)
+- ABID (Analítica de Big Data) - Master Universitario en Ingeniera de Redes y Servicios Telemáticos)
 
 For following this course:
 - Follow the instructions to install the environment: https://github.com/gsi-upm/sitc/blob/master/python/1_1_Notebooks.ipynb (Just install 'conda')
@@ -9,11 +9,13 @@ For following this course:
 - Run in a terminal in the folder sitc: jupyter notebook (and enjoy)
 
 Topics
-* Python: quick introduction to Python
+* Python: a quick introduction to Python
 * ML-1: introduction to machine learning with scikit-learn
 * ML-2: introduction to machine learning with pandas and scikit-learn
+* ML-21: preprocessing and visualizatoin
 * ML-3: introduction to machine learning. Neural Computing
 * ML-4: introduction to Evolutionary Computing
 * ML-5: introduction to Reinforcement Learning
 * NLP: introduction to NLP
 * LOD: Linked Open Data, exercises and example code
+* SNA: Social Network Analysis

sna/0_Intro_Network_Analysis.ipynb

@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "![](images/EscUpmPolit_p.gif \"UPM\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "# Course Notes for Learning Intelligent Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Introduction to Network Analysis\n",
+    " \n",
+    "In this session, we are going to get more insight regarding how to analyze and visualize social networks.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Objectives\n",
+    "\n",
+    "The main objectives of this session are:\n",
+    "* Understanding why networks are important in data science\n",
+    "* Experimenting with network analysis with networkx."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Table of Contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "1. [Home](0_Intro_Network_Analysis.ipynb)\n",
+    "2. [First Steps](1_First_Steps.ipynb)\n",
+    "3. [Working_with_Graphs](2_Working_with_Graphs.ipynb)\n",
+    "4. [Network Analysis](3_Network_Analysis.ipynb)\n",
+    "5. [Social Networks](4_Social_Networks.ipynb)\n",
+    "6. [Pandas integration](5_Pandas.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Licence\n",
+    "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
+}

sna/2a_Florentine_Families_Star_Wars.ipynb

@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "![](images/EscUpmPolit_p.gif \"UPM\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "# Course Notes for Learning Intelligent Systems"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## [Introduction to  Network Analysis](0_Intro_Network_Analysis.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise: Florentine families"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "G_florentine = nx.florentine_families_graph()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Exercise: Star Wars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "\n",
+    "# Taken from https://gist.github.com/codingthat/be03565bd97e789a3835b50235ad562f\n",
+    "# The original dataset is from:\n",
+    "# Gabasova, E. (2016). Star Wars social network. DOI: https://doi.org/10.5281/zenodo.1411479\n",
+    "# \n",
+    "# Simplified by Federico Albanese.\n",
+    "\n",
+    "characters = [\"R2-D2\",\n",
+    "                \"CHEWBACCA\",\n",
+    "                \"C-3PO\",\n",
+    "                \"LUKE\",\n",
+    "                \"DARTH VADER\",\n",
+    "                \"CAMIE\",\n",
+    "                \"BIGGS\",\n",
+    "                \"LEIA\",\n",
+    "                \"BERU\",\n",
+    "                \"OWEN\",\n",
+    "                \"OBI-WAN\",\n",
+    "                \"MOTTI\",\n",
+    "                \"TARKIN\",\n",
+    "                \"HAN\",\n",
+    "                \"DODONNA\",\n",
+    "                \"GOLD LEADER\",\n",
+    "                \"WEDGE\",\n",
+    "                \"RED LEADER\",\n",
+    "                \"RED TEN\"]\n",
+    "\n",
+    "\n",
+    "edges = [(\"CHEWBACCA\", \"R2-D2\"),\n",
+    "        (\"C-3PO\",\"R2-D2\"),\n",
+    "        (\"BERU\", \"R2-D2\"),\n",
+    "        (\"LUKE\", \"R2-D2\"),\n",
+    "        (\"OWEN\", \"R2-D2\"),\n",
+    "        (\"OBI-WAN\", \"R2-D2\"),\n",
+    "        (\"LEIA\", \"R2-D2\"),\n",
+    "        (\"BIGGS\", \"R2-D2\"),\n",
+    "        (\"HAN\", \"R2-D2\"),\n",
+    "        (\"CHEWBACCA\", \"OBI-WAN\"),\n",
+    "        (\"C-3PO\", \"CHEWBACCA\"),\n",
+    "        (\"CHEWBACCA\", \"LUKE\"),\n",
+    "        (\"CHEWBACCA\", \"HAN\"),\n",
+    "        (\"CHEWBACCA\", \"LEIA\"),\n",
+    "        (\"CAMIE\", \"LUKE\"),\n",
+    "        (\"BIGGS\", \"CAMIE\"),\n",
+    "        (\"BIGGS\", \"LUKE\"),\n",
+    "        (\"DARTH VADER\", \"LEIA\"),\n",
+    "        (\"BERU\", \"LUKE\"),\n",
+    "        (\"BERU\", \"OWEN\"),\n",
+    "        (\"BERU\", \"C-3PO\"),\n",
+    "        (\"LUKE\", \"OWEN\"),\n",
+    "        (\"C-3PO\", \"LUKE\"),\n",
+    "        (\"C-3PO\", \"OWEN\"),\n",
+    "        (\"C-3PO\", \"LEIA\"),\n",
+    "        (\"LEIA\", \"LUKE\"),\n",
+    "        (\"BERU\", \"LEIA\"),\n",
+    "        (\"LUKE\", \"OBI-WAN\"),\n",
+    "        (\"C-3PO\", \"OBI-WAN\"),\n",
+    "        (\"LEIA\", \"OBI-WAN\"),\n",
+    "        (\"MOTTI\", \"TARKIN\"),\n",
+    "        (\"DARTH VADER\", \"MOTTI\"),\n",
+    "        (\"DARTH VADER\", \"TARKIN\"),\n",
+    "        (\"HAN\", \"OBI-WAN\"),\n",
+    "        (\"HAN\", \"LUKE\"),\n",
+    "        (\"C-3PO\", \"HAN\"),\n",
+    "        (\"LEIA\", \"MOTT\"),\n",
+    "        (\"LEIA\", \"TARKIN\"),\n",
+    "        (\"HAN\", \"LEIA\"),\n",
+    "        (\"DARTH VADER\", \"OBI-WAN\"),\n",
+    "        (\"DODONNA\", \"GOLD LEADER\"),\n",
+    "        (\"DODONNA\", \"WEDGE\"),\n",
+    "        (\"DODONNA\", \"LUKE\"),\n",
+    "        (\"GOLD LEADER\", \"WEDGE\"),\n",
+    "        (\"GOLD LEADER\", \"LUKE\"),\n",
+    "        (\"LUKE\", \"WEDGE\"),\n",
+    "        (\"BIGGS\", \"LEIA\"),\n",
+    "        (\"LEIA\", \"RED LEADER\"),\n",
+    "        (\"LUKE\", \"RED LEADER\"),\n",
+    "        (\"BIGGS\", \"RED LEADER\"),\n",
+    "        (\"BIGGS\", \"C-3PO\"),\n",
+    "        (\"C-3PO\", \"RED LEADER\"),\n",
+    "        (\"RED LEADER\", \"WEDGE\"),\n",
+    "        (\"GOLD LEADER\", \"RED LEADER\"),\n",
+    "        (\"BIGGS\", \"WEDGE\"),\n",
+    "        (\"RED LEADER\", \"RED TEN\"),\n",
+    "        (\"BIGGS\", \"GOLD LEADER\"),\n",
+    "        (\"LUKE\", \"RED TEN\")]\n",
+    "\n",
+    "G_starWars = nx.Graph()\n",
+    "\n",
+    "\n",
+    "G_starWars.add_nodes_from(characters)\n",
+    "G_starWars.add_edges_from(edges)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n",
+    "In this exercise we are going to practice some of the concepts of the session.\n",
+    "\n",
+    "Answer the following questions using the object *G_starWars* and *G_florentine*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the list of nodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the list of edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw the graph\n",
+    "\n",
+    "Hint.  Use different layouts (circular, spring, ...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Think of interesting micro, meso and macro metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analyze ego networks of interesting nodes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analyze communities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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",
+  "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
+}