twitter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Example Project\n",
    "\n",
    "We're going to import Twitter data into Neo4j and then do various analysis on top of this dataset including plotting, graph algorithms, and graph visualizations.\n",
    "\n",
    "* Import\n",
    "* Plotting / Charting\n",
    "* Graph Algorithms\n",
    "* Graph Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Import\n",
    "\n",
    "The script for importing data from Twitter's API is in [`scripts/twitter.py`](https://github.com/nicolewhite/neo4j-jupyter/blob/master/scripts/twitter.py). This was used to upload tweets that contained either the words \"ddtx16\" or \"datadaytexas\" to Neo4j."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import time\n",
    "import requests\n",
    "from py2neo import Graph, Relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Uniqueness Constraints\n",
    "\n",
    "Neo4j supports uniqueness constraints on given label, property pairs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "graph = Graph()\n",
    "\n",
    "graph.cypher.execute(\"CREATE CONSTRAINT ON (u:User) ASSERT u.username IS UNIQUE\")\n",
    "graph.cypher.execute(\"CREATE CONSTRAINT ON (t:Tweet) ASSERT t.id IS UNIQUE\")\n",
    "graph.cypher.execute(\"CREATE CONSTRAINT ON (h:Hashtag) ASSERT h.name IS UNIQUE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Twitter API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "TWITTER_BEARER = os.environ[\"TWITTER_BEARER\"]\n",
    "\n",
    "headers = dict(accept=\"application/json\", Authorization=\"Bearer \" + TWITTER_BEARER)\n",
    "\n",
    "payload = dict(\n",
    "    count=100,\n",
    "    result_type=\"recent\",\n",
    "    lang=\"en\",\n",
    "    q=sys.argv[1]\n",
    ")\n",
    "\n",
    "base_url = \"https://api.twitter.com/1.1/search/tweets.json?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Find Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def find_tweets(since_id):\n",
    "    payload[\"since_id\"] = since_id\n",
    "    url = base_url + \"q={q}&count={count}&result_type={result_type}&lang={lang}&since_id={since_id}\".format(**payload)\n",
    "\n",
    "    r = requests.get(url, headers=headers)\n",
    "    tweets = r.json()[\"statuses\"]\n",
    "\n",
    "    return tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Upload Tweets to Neo4j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def upload_tweets(tweets):\n",
    "    for t in tweets:\n",
    "        u = t[\"user\"]\n",
    "        e = t[\"entities\"]\n",
    "\n",
    "        tweet = graph.merge_one(\"Tweet\", \"id\", t[\"id\"])\n",
    "        tweet.properties[\"text\"] = t[\"text\"]\n",
    "        tweet.push()\n",
    "\n",
    "        user = graph.merge_one(\"User\", \"username\", u[\"screen_name\"])\n",
    "        graph.create_unique(Relationship(user, \"POSTS\", tweet))\n",
    "\n",
    "        for h in e.get(\"hashtags\", []):\n",
    "            hashtag = graph.merge_one(\"Hashtag\", \"name\", h[\"text\"].lower())\n",
    "            graph.create_unique(Relationship(hashtag, \"TAGS\", tweet))\n",
    "\n",
    "        for m in e.get('user_mentions', []):\n",
    "            mention = graph.merge_one(\"User\", \"username\", m[\"screen_name\"])\n",
    "            graph.create_unique(Relationship(tweet, \"MENTIONS\", mention))\n",
    "\n",
    "        reply = t.get(\"in_reply_to_status_id\")\n",
    "\n",
    "        if reply:\n",
    "            reply_tweet = graph.merge_one(\"Tweet\", \"id\", reply)\n",
    "            graph.create_unique(Relationship(tweet, \"REPLY_TO\", reply_tweet))\n",
    "\n",
    "        ret = t.get(\"retweeted_status\", {}).get(\"id\")\n",
    "\n",
    "        if ret:\n",
    "            retweet = graph.merge_one(\"Tweet\", \"id\", ret)\n",
    "            graph.create_unique(Relationship(tweet, \"RETWEETS\", retweet))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Run It!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "while True:\n",
    "    try:\n",
    "        tweets = find_tweets(since_id=since_id)\n",
    "\n",
    "        if not tweets:\n",
    "            print \"No tweets found.\"\n",
    "            time.sleep(60)\n",
    "            continue\n",
    "\n",
    "        since_id = tweets[0].get(\"id\")\n",
    "        upload_tweets(tweets)\n",
    "\n",
    "        print \"{} tweets uploaded!\".format(len(tweets))\n",
    "        time.sleep(60)\n",
    "\n",
    "    except Exception, e:\n",
    "        print e\n",
    "        time.sleep(60)\n",
    "        continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Visualize the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"400\"\n",
       "            src=\"figure/graph-5a6ac6c1-6f85-4672-a87b-3611600bbe2b.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x10bbb54d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scripts.vis import draw\n",
    "options = {\"User\": \"username\", \"Hashtag\": \"name\"}\n",
    "\n",
    "draw(graph, options, physics=True, limit=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plotting\n",
    "\n",
    "Let's gather some basic insights by creating `plotly` charts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext cypher\n",
    "import plotly.plotly as py\n",
    "from plotly.graph_objs import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## What are the top hashtags?\n",
    "\n",
    "Find hashtags ordered by the number of tweets they've tagged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtag</th>\n",
       "      <th>tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>datadaytexas</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>spark</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>storm</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>datascience</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nlproc</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        hashtag  tweets\n",
       "0  datadaytexas      17\n",
       "1         spark       9\n",
       "2         storm       6\n",
       "3   datascience       5\n",
       "4        nlproc       4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = %cypher MATCH (hashtag:Hashtag)-[:TAGS]->(tweet:Tweet)         \\\n",
    "                 WHERE hashtag.name <> 'ddtx16'                         \\\n",
    "                 RETURN hashtag.name AS hashtag, COUNT(tweet) AS tweets \\\n",
    "                 ORDER BY tweets DESC LIMIT 5\n",
    "        \n",
    "df = result.get_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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tBD759v9XXvo8G0E7NUcTIECAQJ8ENt100z4td+TWqvBuoPDG53oPOuigMn/+/HL++ec/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\nRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = Data([Bar(x=df[\"hashtag\"], y=df[\"tweets\"])])\n",
    "\n",
    "py.image.ishow({'data': data})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## What's the hashtag and mention count distribution?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "423 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hashtags</th>\n",
       "      <th>mentions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hashtags  mentions\n",
       "0         1         2\n",
       "1         1         0\n",
       "2         1         2\n",
       "3         1         1\n",
       "4         1         1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = %cypher MATCH (tweet:Tweet)                                   \\\n",
    "                 RETURN tweet.id,                                      \\\n",
    "                        SIZE((:Hashtag)-[:TAGS]->(tweet)) AS hashtags, \\\n",
    "                        SIZE((tweet)-[:MENTIONS]->(:User)) AS mentions\n",
    "            \n",
    "df = result.get_dataframe()\n",
    "del df[\"tweet.id\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v1wgBAgQIEGgEgZYKvOlIQjq+cOihh8Zpp522R/+rrroqRowYkZ3vXb16dcyY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\nQIAAAQIECBAoUuD/AwOO7H6EU3QfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hashtags = Histogram(x=df[\"hashtags\"], opacity=0.75, name=\"Hashtags\")\n",
    "mentions = Histogram(x=df[\"mentions\"], opacity=0.75, name=\"Mentions\")\n",
    "data = Data([hashtags, mentions])\n",
    "layout = Layout(barmode=\"overlay\")\n",
    "fig = Figure(data=data, layout=layout)\n",
    "\n",
    "py.image.ishow(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Heatmap of hashtag co-occurrence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>h1.name</th>\n",
       "      <th>h2.name</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>datadaytexas</td>\n",
       "      <td>rstats</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>datadaytexas</td>\n",
       "      <td>geekaustin</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spark</td>\n",
       "      <td>storm</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>datadaytexas</td>\n",
       "      <td>ibm</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>datadaytexas</td>\n",
       "      <td>technology</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        h1.name     h2.name  weight\n",
       "0  datadaytexas      rstats       1\n",
       "1  datadaytexas  geekaustin       3\n",
       "2         spark       storm       6\n",
       "3  datadaytexas         ibm       2\n",
       "4  datadaytexas  technology       4"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = %cypher MATCH (h:Hashtag)                           \\\n",
    "                 WHERE h.name <> \"ddtx16\"                    \\\n",
    "                 WITH h, SIZE((h)-[:TAGS]->(:Tweet)) AS tags \\\n",
    "                 ORDER BY tags DESC LIMIT 15                 \\\n",
    "                                                             \\\n",
    "                 WITH COLLECT(h) AS top_hash                 \\\n",
    "                 UNWIND top_hash AS h1                       \\\n",
    "                 UNWIND top_hash AS h2                       \\\n",
    "                                                             \\\n",
    "                 MATCH (h1)-[:TAGS]->(:Tweet)<-[:TAGS]-(h2)  \\\n",
    "                 WHERE h1.name < h2.name                     \\\n",
    "                 RETURN h1.name, h2.name, COUNT(*) AS weight\n",
    "            \n",
    "df = result.get_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "names = list(set(list(df[\"h1.name\"]) + list(df[\"h2.name\"])))\n",
    "heat = [[0 for i in range(len(names))] for j in range(len(names))] \n",
    "\n",
    "for idx, row in df.iterrows():\n",
    "    i = names.index(row[\"h1.name\"])\n",
    "    j = names.index(row[\"h2.name\"])\n",
    "    heat[i][j] = row[\"weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a0zCshDc8vQHhjf0eQHhjz4wzIAABCIQTQHgRXoTXuetBnG9aGzJkiJQoUUIeeuihm3qn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\nwP8ANU0OqcMOEqcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "\n",
    "data = [go.Heatmap(z = heat, x = names, y = names)]\n",
    "py.image.ishow({'data': data})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Graph Algorithms\n",
    "\n",
    "The typical workflow consists of retrieving a subgraph from Neo4j via Cypher and analyzing this graph in `igraph`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## A subgraph of users retweeting other users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<igraph.Graph at 0x1099b3430>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from igraph import Graph as IGraph\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH (user1:User)-[:POSTS]->(retweet:Tweet)-[:RETWEETS]->(tweet:Tweet),\n",
    "      (user2:User)-[:POSTS]->(tweet)\n",
    "RETURN user1.username, user2.username, COUNT(*) AS weight\n",
    "\"\"\"\n",
    "\n",
    "data = graph.cypher.execute(query)\n",
    "\n",
    "ig = IGraph.TupleList(data, weights=True)\n",
    "ig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Which users have the highest betweenness?\n",
    "\n",
    "$betweenness(v) = \\sum_{s, t \\in V} \\frac{\\sigma_{st}(v)}{\\sigma_{st}}$\n",
    "\n",
    "The betweenness centrality of a node $v$ is the number of shortest paths that pass through $v$, $\\sigma_{st}(v)$,  divided by the total number of shortest paths, $\\sigma_{st}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'strataconf', 1922.8634920634918),\n",
       " (u'CaseySoftware', 1716.2927849927842),\n",
       " (u'DataDayTexas', 1366.052813852814),\n",
       " (u'Ellen_Friedman', 1174.9242424242423),\n",
       " (u'aterrel', 1172.3698412698416)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "between = [(node[\"name\"], node.betweenness()) for node in ig.vs]\n",
    "top = sorted(between, key=lambda x: x[1], reverse=True)\n",
    "top[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Which users have the highest closeness?\n",
    "\n",
    "$closeness(v) = \\frac{1}{\\sum_{x} d(v, x)}$\n",
    "\n",
    "The closeness centrality is the reciprocal of a node's farness, or sum of its shortest path distances from all other nodes in the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'CaseySoftware', 0.08967173738991192),\n",
       " (u'DataDayTexas', 0.08945686900958466),\n",
       " (u'clarifyio', 0.08874801901743265),\n",
       " (u'aterrel', 0.08825847123719464),\n",
       " (u'Ellen_Friedman', 0.0875684128225176)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close = [(node[\"name\"], node.closeness()) for node in ig.vs]\n",
    "top = sorted(close, key=lambda x: x[1], reverse=True)\n",
    "top[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Community detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters = IGraph.community_walktrap(ig, weights=\"weight\")\n",
    "clusters = clusters.as_clustering()\n",
    "len(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': u'marksgorman', 'label': u'marksgorman'},\n",
       " {'id': u'venkatesh_cr', 'label': u'venkatesh_cr'},\n",
       " {'id': u'hadleywickham', 'label': u'hadleywickham'},\n",
       " {'id': u'JMayData', 'label': u'JMayData'},\n",
       " {'id': u'datanerd_jaya', 'label': u'datanerd_jaya'}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = [node[\"name\"] for node in ig.vs]\n",
    "nodes = [{\"id\": x, \"label\": x} for x in nodes]\n",
    "nodes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'group': 0, 'id': u'marksgorman', 'label': u'marksgorman'},\n",
       " {'group': 1, 'id': u'venkatesh_cr', 'label': u'venkatesh_cr'},\n",
       " {'group': 2, 'id': u'hadleywickham', 'label': u'hadleywickham'},\n",
       " {'group': 2, 'id': u'JMayData', 'label': u'JMayData'},\n",
       " {'group': 1, 'id': u'datanerd_jaya', 'label': u'datanerd_jaya'}]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for node in nodes:\n",
    "    idx = ig.vs.find(name=node[\"id\"]).index\n",
    "    node[\"group\"] = clusters.membership[idx]\n",
    "    \n",
    "nodes[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'from': u'marksgorman', 'to': u'venkatesh_cr'},\n",
       " {'from': u'hadleywickham', 'to': u'JMayData'},\n",
       " {'from': u'datanerd_jaya', 'to': u'ramhiser'},\n",
       " {'from': u'Chuparkoff', 'to': u'RobertsPaige'},\n",
       " {'from': u'Ellen_Friedman', 'to': u'OReillyMedia'}]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges = [{\"from\": x[0], \"to\": x[1]} for x in data]\n",
    "edges[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"400\"\n",
       "            src=\"figure/graph-ce4f7cdc-eac0-43ee-b859-a44ee6d0ab34.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x10f84e2d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scripts.vis import vis_network\n",
    "vis_network(nodes, edges, physics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Graph Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## jgraph\n",
    "\n",
    "Let's visualize (in 3D!) the structure of users retweeting and replying to other users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"graph-a4a87807-1fce-46f9-87e7-def15fa147c7\"></div>\n",
       "           <script type=\"text/javascript\">\n",
       "           require.config({baseUrl: '/',\n",
       "                             paths: {jgraph: ['nbextensions/jgraph.min', 'https://rawgit.com/patrickfuller/jgraph/master/js/build/jgraph.min']}});\n",
       "           require(['jgraph'], function () {\n",
       "               var $d = $('#graph-a4a87807-1fce-46f9-87e7-def15fa147c7');\n",
       "               $d.width(600); $d.height(400);\n",
       "               $d.jgraph = jQuery.extend({}, jgraph);\n",
       "               $d.jgraph.create($d, {nodeSize: 2.000000,\n",
       "                                     edgeSize: 0.250000,\n",
       "                                     defaultNodeColor: '0x5bc0de',\n",
       "                                     defaultEdgeColor: '0xaaaaaa',\n",
       "                                     shader: 'basic',\n",
       "                                     z: 100,\n",
       "                                     runOptimization: true,\n",
       "                                     directed: true,\n",
       "                                     showSave: false});\n",
       "               $d.jgraph.draw({\n",
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       "    ],\n",
       "    \"nodes\": {\n",
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       "    }\n",
       "});\n",
       "\n",
       "               $d.resizable({\n",
       "                   aspectRatio: 600 / 400,\n",
       "                   resize: function (evt, ui) {\n",
       "                       $d.jgraph.renderer.setSize(ui.size.width,\n",
       "                                                  ui.size.height);\n",
       "                   }\n",
       "               });\n",
       "           });\n",
       "           </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import jgraph\n",
    "\n",
    "query = \"\"\"\n",
    "MATCH (user1:User)-[:POSTS]->(:Tweet)-[:RETWEETS|REPLY_TO]->(:Tweet)<-[:POSTS]-(user2:User)\n",
    "RETURN ID(user1), ID(user2)\n",
    "\"\"\"\n",
    "\n",
    "data = graph.cypher.execute(query)\n",
    "tup = [tuple(x) for x in data]\n",
    "\n",
    "jgraph.draw(tup)"
   ]
  }
 ],
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