Visualizing Cyber Attacks with Suricata and Zeek using Brim and NetworkX

Visualizing Cyber Attacks with Suricata and Zeek using Brim and NetworkX.ipynb

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    "# Visualizing Suricata and Zeek using Brim and NetworkX\n",
    "\n",
    "Welcome to our second article on Brim's Data Science blog. [In the first article in this series](http://medium.com/brim-securitys-knowledge-funnel/visualizing-ip-traffic-with-brim-zeek-and-networkx-3844a4c25a2f)\n",
    ", we learned how to use Brim's python library to fetch Zeek data into Pandas to visualize network activity<br><br>\n",
    "\n",
    "Today we're going to build on what we learned last time. Instead of just graphing out Zeek data, we're going to fuse Zeek and Suricata data together. We're also going to improve how we visualize our network graph to gain some useful insights.\n",
    "\n",
    "#### About Brim\n",
    "If you're new to Brim: \n",
    "\n",
    "- [Brim](https://www.brimsecurity.com/) is an open source tool to search and analyze pcaps, Zeek and Suricata logs. \n",
    "- [Zeek](https://zeek.org/) is the most popular open source platform for network security monitoring. \n",
    "- [Suricata](https://suricata-ids.org/) is an open source threat detection engine (commonly called Intrusion Detection and Prevention Systems). \n",
    "\n",
    "Brim can import raw pcaps to enrich and analyze them with embedded Zeek and Suricata engines, and makes them available for search and analysis in the Brim app. Brim also provides a python library to support data science use cases and pipelines. We'll be using Brim to create graph networks for network and threat activity.\n",
    "\n",
    "#### Instructions and Prep\n",
    "You can download Brim [here](https://www.brimsecurity.com/download/)<br>\n",
    "Installation instructons for Brim are [here](https://github.com/brimsec/brim/wiki/Installation)<br>\n",
    "Instructions for Brim's python library can be found [here](https://gist.github.com/orochford/a82ae16f42894d6fd7a85c60115d3922)<br>\n",
    "We're going to be using [NetworkX](https://networkx.org/) and [Jupyter Notebook](https://jupyter.org/)<br>\n",
    "Todays [malware sample](https://www.malware-traffic-analysis.net/2021/02/17/2021-02-17-Trickbot-gtag-rob13-infection-in-AD-environment.pcap.zip) (password: infected) is courtesy of [Malware Traffic Analysis](https://www.malware-traffic-analysis.net/2021/02/17/index.html)<br>\n",
    "\n",
    "\n",
    "### Getting started\n",
    "\n",
    "First we'll import all of our required libraries"
   ]
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   "id": "theoretical-status",
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   "source": [
    "# Import libraries and dependencies\n",
    "import pandas as pd\n",
    "import zqd as zqd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import ipaddress\n",
    "import math\n",
    "from collections import Counter\n",
    "import networkx.algorithms.community as nxcom"
   ]
  },
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"
    }
   },
   "cell_type": "markdown",
   "id": "correct-handbook",
   "metadata": {},
   "source": [
    "### Selecting the Brim Space\n",
    "We'll select our Brim space to work with. You can find your space in the Brim app in the upper left corner. You can right-click the space and copy and paste the full name.<br>\n",
    "<img src=\"attachment:image.png\" width=\"400\" align=\"left\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "pleased-daily",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Brim Space to query\n",
    "space=\"2021-02-17-Trickbot-gtag-rob13-infection-in-AD-environment.pcap\""
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "criminal-jordan",
   "metadata": {},
   "source": [
    "### Z Queries\n",
    "We're also going to need to define the Z queries we want to use. The first query is similar to the one we used in the last article. We filter for Zeek's \"conn\" stream, and then cut out the ```id.orig_h``` (source), ```id.resp_h``` (target), and ```id.resp_p``` (target port) and count for unique occurences.\n",
    "\n",
    "```_path=conn | count() by id.orig_h, id.resp_h, id.resp_p | sort id.orig_h, id.resp_h, id.resp_p```\n",
    "\n",
    "Note that we're using ```count()``` to aggregate the logs. While this means that we'll lose some of the fidelity for calculating graph attributes such as clustering, it also pushes some of the heavy processing to ZQ and Brim.\n",
    "\n",
    "For fetching the suricata data, we'll be using the following query:\n",
    "\n",
    "```event_type=alert | count() by src_ip, dest_ip, dest_port, alert.severity, alert.signature | sort src_ip, dest_ip, dest_port, alert.severity, alert.signature```\n",
    "\n",
    "The query filters for suricata alert ```event_type=alert```, counts and sorts by ```src_ip``` (source), ```dest_ip``` (target), ```dest_port``` (port), and ```alert signature```.\n",
    "\n",
    "![image.png](attachment:image.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ranging-vulnerability",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ZQL queries to send to ZQD\n",
    "# Z query to fetch Zeek connection data to create our network connection graph\n",
    "zql = '_path=conn | count() by id.orig_h, id.resp_h, id.resp_p | sort id.orig_h, id.resp_h, id.resp_p'\n",
    "# Z query to fetch Suricata alerts including the count of alerts per source:destination tuple\n",
    "zql2 = 'event_type=alert | count() by src_ip, dest_ip, dest_port, alert.severity, alert.signature | sort src_ip, dest_ip, dest_port, alert.severity, alert.signature'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "touched-objective",
   "metadata": {},
   "source": [
    "### Connecting to Brim via ZQ\n",
    "\n",
    "Next we'll connect to Brim by creating a ZQD client instance and sending our Z queries off. We'll also need to flatten\n",
    "any nested data fields using pandas ```json_normalize()``` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "touched-tennis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ZQD client instance\n",
    "c = zqd.Client()  \n",
    "\n",
    "# Send ZQL query to ZQD\n",
    "try:\n",
    "    s = c.search(space, zql)\n",
    "except:\n",
    "    print(\"Failed to connect to ZQD, check that ZQD is running and space %s exists\" % space)\n",
    "# Create a dataframe and flatten json/dictionary\n",
    "df = pd.json_normalize(s)\n",
    "\n",
    "# Send ZQL2 query to ZQD\n",
    "try:\n",
    "    s2 = c.search(space, zql2)\n",
    "except:\n",
    "    print(\"Failed to connect to ZQD, check that ZQD is running and space %s exists\" % space) \n",
    "# Create a dataframe and flatten json/dictionary\n",
    "df2 = pd.json_normalize(s2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chronic-crowd",
   "metadata": {},
   "source": [
    "### Our two DataFrames\n",
    "If the queries executed correctly, we now have two dataframes, \"df\" containing the Zeek results, and \"df2\" containing the Suricata alert data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ceramic-radical",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Records in df: 73\n",
      "\n",
      "Records in df2: 29\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Records in df: %s\\n\" % len(df))\n",
    "print(\"Records in df2: %s\\n\" % len(df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expensive-content",
   "metadata": {},
   "source": [
    "## Creating a merged  DataFrame\n",
    "\n",
    "Before we can use our data to create a network graph, we need to do some data preperation.\n",
    "\n",
    "First we're going to prepare two dataframes to merge the Zeek and Suricata data, called \"dfz\" and \"dfs\".\n",
    "\n",
    "We'll assign ```id.orig_h``` and ```src_ip``` to a column named ```source``` to make indexing easier. We also need to change our ```count``` columns for both data sources, or we'll have duplicate fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "immune-period",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        source        target  port  conncount\n",
      "0  10.2.17.101  3.222.126.94    80          1\n",
      "1  10.2.17.101     10.2.17.1   445          1\n",
      "2  10.2.17.101     10.2.17.2    53         97\n",
      "3  10.2.17.101     10.2.17.2    88         27\n",
      "4  10.2.17.101     10.2.17.2   123          5\n",
      "      source       target   port  alertcount  severity\n",
      "0  10.2.17.2  10.2.17.101  49680           1         3\n",
      "1  10.2.17.2  10.2.17.101  49687           1         3\n",
      "2  10.2.17.2  10.2.17.101  49704           1         3\n",
      "3  10.2.17.2  10.2.17.101  49709           1         3\n",
      "4  10.2.17.2  10.2.17.101  49721           1         3\n"
     ]
    }
   ],
   "source": [
    "# Create our two dataframes from the zeek and suricata data\n",
    "dfz = pd.DataFrame({\"source\":df[\"id.orig_h\"], \"target\":df[\"id.resp_h\"], \"port\":df[\"id.resp_p\"], \"conncount\":df[\"count\"]})\n",
    "dfs = pd.DataFrame({\"source\":df2[\"src_ip\"], \"target\":df2[\"dest_ip\"], \"port\":df2[\"dest_port\"], \"alertcount\":df2[\"count\"], \"severity\":df2['alert.severity']})\n",
    "# Print the first 5 records\n",
    "print(dfz.head(5))\n",
    "print(dfs.head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comprehensive-exploration",
   "metadata": {},
   "source": [
    "### Merging the DataFrames\n",
    "\n",
    "Now we need to merge the two dataframes, using [pandas.concat()](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.concat.html). \n",
    "\n",
    "We should end up with a merged data frame, indexed by ```source```, ```target```, and ```port```, with the associated count of alerts and connection transactions attached to each connection. Also note we're setting ```ignore_index=True``` to maintain a continuous index value across the rows in the new appended data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "failing-frame",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        source        target  port  conncount  alertcount  severity\n",
      "0  10.2.17.101  3.222.126.94    80        1.0         NaN       NaN\n",
      "1  10.2.17.101     10.2.17.1   445        1.0         NaN       NaN\n",
      "2  10.2.17.101     10.2.17.2    53       97.0         NaN       NaN\n",
      "3  10.2.17.101     10.2.17.2    88       27.0         NaN       NaN\n",
      "4  10.2.17.101     10.2.17.2   123        5.0         NaN       NaN\n",
      "              source       target   port  conncount  alertcount  severity\n",
      "97    179.191.108.58  10.2.17.101  49863        NaN         1.0       3.0\n",
      "98    179.191.108.58  10.2.17.101  50130        NaN         1.0       3.0\n",
      "99    179.191.108.58  10.2.17.101  50141        NaN         1.0       3.0\n",
      "100  195.123.208.170  10.2.17.101  49864        NaN         1.0       1.0\n",
      "101  195.123.208.170  10.2.17.101  49864        NaN         2.0       2.0\n"
     ]
    }
   ],
   "source": [
    "# Merge the two to create our final dataframe\n",
    "dfc = pd.concat([dfz, dfs], ignore_index=True)\n",
    "print(dfc.head(5))\n",
    "print(dfc.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "informed-hotel",
   "metadata": {},
   "source": [
    "### Populate NaN fields\n",
    "\n",
    "Because there are usually far more connections than suricata alerts, we'll end up with many records where the ```alertcount``` and ```severity``` will be ```NaN```, we're going to populate all ```NaN fields``` with ```0```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "charged-smart",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        source        target  port  conncount  alertcount  severity\n",
      "0  10.2.17.101  3.222.126.94    80        1.0         0.0       0.0\n",
      "1  10.2.17.101     10.2.17.1   445        1.0         0.0       0.0\n",
      "2  10.2.17.101     10.2.17.2    53       97.0         0.0       0.0\n",
      "3  10.2.17.101     10.2.17.2    88       27.0         0.0       0.0\n",
      "4  10.2.17.101     10.2.17.2   123        5.0         0.0       0.0\n",
      "              source       target   port  conncount  alertcount  severity\n",
      "97    179.191.108.58  10.2.17.101  49863        0.0         1.0       3.0\n",
      "98    179.191.108.58  10.2.17.101  50130        0.0         1.0       3.0\n",
      "99    179.191.108.58  10.2.17.101  50141        0.0         1.0       3.0\n",
      "100  195.123.208.170  10.2.17.101  49864        0.0         1.0       1.0\n",
      "101  195.123.208.170  10.2.17.101  49864        0.0         2.0       2.0\n"
     ]
    }
   ],
   "source": [
    "## Do some data cleaning and prep\n",
    "# Fill NaN data\n",
    "dfc = dfc.fillna(0)\n",
    "print(dfc.head(5))\n",
    "print(dfc.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "virgin-stock",
   "metadata": {},
   "source": [
    "### Recast types\n",
    "\n",
    "```Pandas.concat()``` will type all numbers as floats, so we'll recast these as ```int64```. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "talented-prairie",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Recast float types to int\n",
    "dfc[\"conncount\"] = dfc[\"conncount\"].astype(\"int64\")\n",
    "dfc[\"alertcount\"] = dfc[\"alertcount\"].astype(\"int64\")\n",
    "dfc[\"severity\"] = dfc[\"severity\"].astype(\"int64\")\n",
    "dfc[\"port\"] = dfc[\"port\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "underlying-memorial",
   "metadata": {},
   "source": [
    "### Calculate weights\n",
    "\n",
    "We're also going to calculate some weights based off of the connection and alert counts. It's not a fantastically sophisticated calculation: we divide 10 by the maximum value for count, multiply it by the count and add 0.1 (to avoid a divide by zero error). This will give us a range from 0.1 - 10.1. \n",
    "\n",
    "We are going to calculate an ```Alertweight```  weight for force-directed graphs.<br>\n",
    "And we'll also be calculating a ```connweight``` to colorize the edges representing the zeek conn transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "exciting-police",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the weights based off of the count of unique Suricata alerts\n",
    "maxalertcount=dfc[\"alertcount\"].max()\n",
    "maxconncount=dfc[\"conncount\"].max()\n",
    "dfc[\"alertweight\"] = dfc.apply(lambda x: 10/maxalertcount*x.alertcount+0.1, axis=1)\n",
    "dfc[\"connweight\"] = dfc.apply(lambda x: 10/maxconncount*x.conncount+0.1, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exotic-reducing",
   "metadata": {},
   "source": [
    "Let's print out some data about our DataFrame to validate that our calculations were sucessful and have been applied to the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "divided-arrest",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n",
      "10.1\n",
      "0.1\n",
      "10.1\n",
      "        source        target  port  conncount  alertcount  severity  \\\n",
      "0  10.2.17.101  3.222.126.94    80          1           0         0   \n",
      "1  10.2.17.101     10.2.17.1   445          1           0         0   \n",
      "2  10.2.17.101     10.2.17.2    53         97           0         0   \n",
      "3  10.2.17.101     10.2.17.2    88         27           0         0   \n",
      "4  10.2.17.101     10.2.17.2   123          5           0         0   \n",
      "\n",
      "   alertweight  connweight  \n",
      "0          0.1    0.203093  \n",
      "1          0.1    0.203093  \n",
      "2          0.1   10.100000  \n",
      "3          0.1    2.883505  \n",
      "4          0.1    0.615464  \n",
      "              source       target   port  conncount  alertcount  severity  \\\n",
      "97    179.191.108.58  10.2.17.101  49863          0           1         3   \n",
      "98    179.191.108.58  10.2.17.101  50130          0           1         3   \n",
      "99    179.191.108.58  10.2.17.101  50141          0           1         3   \n",
      "100  195.123.208.170  10.2.17.101  49864          0           1         1   \n",
      "101  195.123.208.170  10.2.17.101  49864          0           2         2   \n",
      "\n",
      "     alertweight  connweight  \n",
      "97           2.1         0.1  \n",
      "98           2.1         0.1  \n",
      "99           2.1         0.1  \n",
      "100          2.1         0.1  \n",
      "101          4.1         0.1  \n"
     ]
    }
   ],
   "source": [
    "## Debug output\n",
    "# Show the min/max values for both calculated weights\n",
    "print(dfc.alertweight.min())\n",
    "print(dfc.alertweight.max())\n",
    "print(dfc.connweight.min())\n",
    "print(dfc.connweight.max())\n",
    "# Show first and last 5 records\n",
    "print(dfc.head(5))\n",
    "print(dfc.tail(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unusual-difficulty",
   "metadata": {},
   "source": [
    "## Creating our Graph\n",
    "\n",
    "Our DataFrame is now ready to feed into a graph. We're going to actually create two graphs, a [Multidirected graph](https://networkx.org/documentation/stable/reference/classes/multidigraph.html) , that can store multiple parellel and directed edges, allowing us to model connections to different ports and with different suricata signatures, as well as whether they were sent or received by a node.\n",
    "\n",
    "The second graph is a standard [undirected graph](https://networkx.org/documentation/stable/reference/classes/graph.html) which we'll use for algorithmic graph analysis.\n",
    "\n",
    "Note how we define ```port``` as the edge key, and we keep all of the edge attributes such as severity and alert weights by definng ```edge_attr=True```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "parallel-orlando",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create graphs\n",
    "# Create our graph from our pandas dataframe\n",
    "G = nx.from_pandas_edgelist(dfc, source=\"source\", target=\"target\", edge_key=\"port\", edge_attr=True, create_using=nx.MultiDiGraph())\n",
    "G2 = nx.from_pandas_edgelist(dfc, source=\"source\", target=\"target\", edge_attr=True, create_using=nx.Graph())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "architectural-halloween",
   "metadata": {},
   "source": [
    "### Add node attibutes\n",
    "\n",
    "We also need to add attributes to our node list, as this is not done automatically by ```networkx.from_pandas_edgelist()```\n",
    "We'll add the ```alertcount```, ```severity```, ```alertweight```, and ```connweight```, so that we can use these as weights when we draw our nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "operational-french",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add our node attributes\n",
    "# List of desired source attributes:\n",
    "src_attributes = [ \"alertcount\",\"severity\", \"alertweight\", \"connweight\"]\n",
    "\n",
    "# Iterate over df rows and set source node attributes:\n",
    "for index, row in dfc.iterrows():\n",
    "    src_attr_dict = {k: row.to_dict()[k] for k in src_attributes}    \n",
    "    G.nodes[row[\"source\"]].update(src_attr_dict)\n",
    "    G.nodes[row[\"target\"]].update(src_attr_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "olive-bedroom",
   "metadata": {},
   "source": [
    "### Adjust for graph size\n",
    "\n",
    "NetworkX is not the best tool for visualizing network graphs with many nodes (it is designed primarily for analysis, see the section below on [Large Networks](#large_networks)). 1000 nodes is a safe limit for us to visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "cognitive-intention",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Small Graph: 62 figsize: (18, 15)\n"
     ]
    }
   ],
   "source": [
    "# Check how many nodes the graph contains to set attributes for figure size\n",
    "n = len(G.nodes)\n",
    "if n > 1000:\n",
    "    largegraph=True\n",
    "    fig_size = (50,40)\n",
    "    print(\"Large Graph: %s , using large graph functions\" % n)\n",
    "if n <= 500 > 999:\n",
    "    fig_size = (40,30)\n",
    "    largegraph=False\n",
    "    print(\"Small Graph: %s figsize: %s\" % (n, fig_size))\n",
    "if n <= 250  > 499 :\n",
    "    fig_size = (30,20)\n",
    "    largegraph=False\n",
    "    print(\"Small Graph: %s figsize: %s\" % (n, fig_size))\n",
    "if n < 100 > 0:\n",
    "    fig_size = (18,15)\n",
    "    largegraph=False\n",
    "    print(\"Small Graph: %s figsize: %s\" % (n, fig_size))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "greater-aging",
   "metadata": {},
   "source": [
    "## Analyzing our graphs\n",
    "\n",
    "Awesome - our graphs are constructed, so now we can start analzying them to get a feel for what our data contains.\n",
    "\n",
    "We're going to look at a few different graph attributes and metrics.\n",
    "\n",
    "[Graph Density](https://en.wikipedia.org/wiki/Dense_graph): A dense graph is a graph in which the number of edges is close to the maximal number of edges, i.e. with almost all nodes connected. Density is measured between 0 and 1.<br>\n",
    "[Graph Transivity](https://www.sci.unich.it/~francesc/teaching/network/transitivity.html): Transitivity is the overall probability for the network to have adjacent nodes interconnected.<br>\n",
    "[Average Clustering](https://en.wikipedia.org/wiki/Clustering_coefficient): The average clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Nodes tend to create tightly knit groups characterised by a relatively high density of ties.<br>\n",
    "[Greedy Modularity Communities](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html): Find communities in graph using Clauset-Newman-Moore greedy modularity maximization.\n",
    "\n",
    "We're also verifying that the graph is directed,and if it is already weighted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "acute-minute",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph analysis\n",
      "\n",
      "Multi-edge directed Graph\n",
      "\n",
      "Number of nodes: 62\t\n",
      "Number of edges: 92\n",
      "\n",
      "Graph density: 0.024325753569539928\n",
      "\n",
      "Graph is directed: True\n",
      "\n",
      "Graph is weighted: False\n",
      "\n",
      "Undirected Graph\n",
      "\n",
      "Transivity: 0\n",
      "Average clustering coefficient: 0.0\n",
      "Greedy Modularity Communities: 1\n"
     ]
    }
   ],
   "source": [
    "print(\"Graph analysis\\n\")\n",
    "print(\"Multi-edge directed Graph\\n\")\n",
    "print(\"Number of nodes: %s\\t\" % len(G.nodes))\n",
    "print(\"Number of edges: %s\\n\" % len(G.edges))\n",
    "print(\"Graph density: %s\\n\" % nx.density(G))\n",
    "print(\"Graph is directed: %s\\n\" % G.is_directed())\n",
    "print(\"Graph is weighted: %s\\n\" % nx.is_weighted(G))\n",
    "print(\"Undirected Graph\\n\")\n",
    "print(\"Transivity: %s\" % nx.transitivity(G2))\n",
    "print(\"Average clustering coefficient: %s\" % nx.average_clustering(G2))\n",
    "communities = sorted(nxcom.greedy_modularity_communities(G2), key=len, reverse=True)\n",
    "print(\"Greedy Modularity Communities: %s\" % len(communities))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utility-fabric",
   "metadata": {},
   "source": [
    "## Drawing our Graph\n",
    "\n",
    "Now that we've created our graph, we can start drawing. We'll just pass the graph to different layouts to get a general feel for what's in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "changed-lending",
   "metadata": {},
   "outputs": [
    {
     "data": {