Chapter 7 - Ties Strength & Resilience.ipynb

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   "source": [
    "<span>\n",
    "<b>Author:</b> <a href=\"http://about.giuliorossetti.net\">Giulio Rossetti</a><br/>\n",
    "<b>Python version:</b>  >=3.7<br/>\n",
    "<b>Networkx version:</b>  >=2.3<br/>\n",
    "<b>Last update:</b> 24/02/2021\n",
    "</span>\n",
    "\n",
    "<a id='top'></a>\n",
    "# *Chapter 7: Ties Strength & Resilience*\n",
    "\n",
    "In this notebook are introduced some of the main analysis discussed in lecture 7.\n",
    "\n",
    "**Note:** this notebook is purposely not 100% comprehensive, it only discusses the basic things you need to get started. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "source": [
    "Reading Game of Thrones Season 6 edge data and defining the graph g (useful for further operations)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_net_w(filename):\n",
    "    g = nx.Graph()\n",
    "    with open(filename) as f:\n",
    "        f.readline()\n",
    "        for l in f:\n",
    "            l = l.split(\",\")\n",
    "            g.add_edge(l[0], l[1], weight=int(l[2]))\n",
    "    return g\n",
    "\n",
    "# Game of Thrones data\n",
    "season = 6\n",
    "g = read_net_w(f'data/asioaf/got-s{season}-edges.csv')"
   ]
  },
  {
   "source": [
    "## Bridges and Local Bridges"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Checking if graph has bridges"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "nx.has_bridges(g)"
   ]
  },
  {
   "source": [
    "Generating all bridges in the graph"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[('JON', 'SAM'),\n",
       " ('JON', 'JEOR'),\n",
       " ('MISSANDEI', 'KRAZNYS_MO_NAKLOZ'),\n",
       " ('TYRION', 'AERYS'),\n",
       " ('DAVOS', 'SHIREEN'),\n",
       " ('SAM', 'MAESTER_AEMON'),\n",
       " ('CERSEI', 'MOUNTAIN'),\n",
       " ('TORMUND', 'MANCE'),\n",
       " ('HOUND', 'GATINS'),\n",
       " ('BRYNDEN', 'MINISA'),\n",
       " ('EDMURE', 'HOSTER'),\n",
       " ('QYBURN', 'ARTHUR_CHILD'),\n",
       " ('NED', 'RICKARD_STARK')]"
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "list(nx.bridges(g))"
   ]
  },
  {
   "source": [
    "Generating all local bridges in the graph and computing the span (i.e., the shortest path length between the endpoints if the local bridge is removed)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "[('JON', 'SAM', inf),\n",
       " ('JON', 'JEOR', inf),\n",
       " ('MISSANDEI', 'KRAZNYS_MO_NAKLOZ', inf),\n",
       " ('TYRION', 'AERYS', inf),\n",
       " ('VARYS', 'STANNIS', 3),\n",
       " ('ARYA', 'WALDER', 3),\n",
       " ('DAVOS', 'SHIREEN', inf),\n",
       " ('SAM', 'MAESTER_AEMON', inf),\n",
       " ('CERSEI', 'MOUNTAIN', inf),\n",
       " ('TORMUND', 'MANCE', inf),\n",
       " ('HOUND', 'GATINS', inf),\n",
       " ('BRYNDEN', 'MINISA', inf),\n",
       " ('EDMURE', 'HOSTER', inf),\n",
       " ('QYBURN', 'ARTHUR_CHILD', inf),\n",
       " ('NED', 'RICKARD_STARK', inf)]"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "list(nx.local_bridges(g, with_span=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tie Strength"
   ]
  },
  {
   "source": [
    "Measuring Tie Strength for each pair of nodes in the graph through Neighborhood Overlap"
   ],
   "cell_type": "markdown",
   "metadata": {}
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def node_overlap(g):\n",
    "    for u, v in g.edges():\n",
    "        n_u = set(g.neighbors(u)) # set of u neighbors\n",
    "        n_v = set(g.neighbors(v)) # set of v neighbors\n",
    "        overlap = len(n_u & n_v) / len(n_u | n_v) # Neighborhood Overlap\n",
    "        g[u][v]['overlap'] = overlap\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = node_overlap(g)"
   ]
  },
  {
   "source": [
    "Plotting the KDE (Kernel Density Estimation) of Neighborhood Overlap"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
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   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "weights = [e[-1]['overlap'] for e in g.edges(data=True)] \n",
    "pd.DataFrame(weights)[0].plot.kde()\n",
    "plt.xlabel(\"Neighborhood Overlap\")\n",
    "plt.xlim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "Plotting the KDE (Kernel Density Estimation) of Interactions Weights"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "weights_got = [d['weight'] for u,v,d in g.edges(data=True)]\n",
    "pd.DataFrame(weights_got)[0].plot.kde()\n",
    "plt.xlabel(\"Interaction Weights\")\n",
    "plt.xlim(0,max(weights_got))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Resilience\n",
    "In the following we:\n",
    "- generate Random and Scale-free Network\n",
    "- compute Reslience for both Networks (through molloy_reed and breakdown_threshold)\n",
    "- plot Game of Thrones graph Resilience under Random Failure and Targeted Attacks"
   ]
  },
  {
   "source": [
    "Generating Networks"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random\n",
    "er = nx.erdos_renyi_graph(1000, 0.001)\n",
    "# Scale-free\n",
    "ba = nx.barabasi_albert_graph(1000, 2)"
   ]
  },
  {
   "source": [
    "Computing node degree for both ER and BA Networks"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "er_degs = list(dict(er.degree()).values())\n",
    "ba_degs = list(dict(ba.degree()).values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining Molloy-Reed threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def molloy_reed(degrees):\n",
    "    return (np.mean(degrees)*(np.mean(degrees)+1))/np.mean(degrees)\n",
    "\n",
    "def breakdown_threshold(degrees):\n",
    "    K = molloy_reed(degrees)\n",
    "    return 1 - (1/(K-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2.042"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "molloy_reed(er_degs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.040307101727447"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "breakdown_threshold(er_degs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale-free network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "4.992"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "molloy_reed(ba_degs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7494989979959921"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "breakdown_threshold(ba_degs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Failures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_node(g): # select a random node from graph\n",
    "    return [np.random.choice(g.nodes())]\n",
    "\n",
    "def dismantle(g, function, **args): # incrementally removes node from a graph and computes size of connected components\n",
    "    total_nodes = g.number_of_nodes()\n",
    "    removed_nodes = []\n",
    "    components = []\n",
    "    while len(g.nodes()) > 1:\n",
    "        n = function(g, **args)[0]\n",
    "        g.remove_node(n)\n",
    "        removed_nodes.append((len(removed_nodes)+1)/total_nodes)\n",
    "        comps = list(nx.connected_components(g))\n",
    "        g_size = 0\n",
    "        if len(comps)>0:\n",
    "            g_size  = max([len(c)for c in comps])/total_nodes\n",
    "        components.append(g_size)\n",
    "    return removed_nodes, components\n",
    "\n",
    "def get_sorted_nodes(g, score, reverse=True): # sort nodes\n",
    "    nodes = score(g)\n",
    "    if isinstance(nodes, dict):\n",
    "        nodes = [(k, v) for k, v in nodes.items()]\n",
    "    srt = sorted(nodes, key = lambda k: k[1], reverse = reverse)\n",
    "    return [x[0] for x in srt]\n",
    "\n",
    "def plot_dismantle(x, y):\n",
    "    plt.plot(x, y)\n",
    "    plt.xlabel(\"Removed Nodes\")\n",
    "    plt.ylabel(\"Giant Component size\")\n",
    "    plt.show()"
   ]
  },
  {
   "source": [
    "Random Failure:\n",
    "- Giant component size has a consistent decrease when a big fraction of nodes is removed"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "h = g.copy()\n",
    "rn, comps = dismantle(h, random_node)\n",
    "plot_dismantle(rn, comps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Targeted Attacks"
   ]
  },
  {
   "source": [
    "Targeted Node attack (hubs are removed first)\n",
    "- Giant component size has a consistent decrease when a small fraction of nodes is removed"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "h = g.copy()\n",
    "rn, comps = dismantle(h, get_sorted_nodes, score=nx.degree)\n",
    "plot_dismantle(rn, comps)"
   ]
  },
  {
   "source": [
    "Targeted Edge Attack (based on Edge Betweenness Centrality)\n",
    "- Giant component size has a consistent decrease when a small fraction of nodes is removed"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "h = g.copy()\n",
    "rn, comps = dismantle(h, get_sorted_nodes, score=nx.betweenness_centrality)\n",
    "plot_dismantle(rn, comps)"
   ]
  },
  {
   "source": [
    "Targeted Edge Attack (based on Edge Harmonic Centrality)\n",
    "- Giant component size has a consistent decrease when a small fraction of nodes is removed"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "h = g.copy()\n",
    "rn, comps = dismantle(h, get_sorted_nodes, score=nx.harmonic_centrality)\n",
    "plot_dismantle(rn, comps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}