arachne/analysis_pipeline_sample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1992821c",
   "metadata": {},
   "source": [
    "### **Introduction to Arachne**\n",
    "Arachne is a Python package for graph analysis that is built as an extension to Arkouda, a Python package for analysis on tabular data, akin to NumPy and Pandas. In this notebook we will show examples on how to run each algorithm that has been implemented on different types of graphs: undirected, directed, and property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f651e065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    _         _                   _       \n",
      "   / \\   _ __| | _____  _   _  __| | __ _ \n",
      "  / _ \\ | '__| |/ / _ \\| | | |/ _` |/ _` |\n",
      " / ___ \\| |  |   < (_) | |_| | (_| | (_| |\n",
      "/_/   \\_\\_|  |_|\\_\\___/ \\__,_|\\__,_|\\__,_|\n",
      "                                          \n",
      "\n",
      "Client Version: v2024.06.21+0.gcf6eeacde.dirty\n"
     ]
    }
   ],
   "source": [
    "import arkouda as ak\n",
    "import arachne as ar\n",
    "import scipy as sp\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "671cb378",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "connected to arkouda server tcp://*:5555\n"
     ]
    }
   ],
   "source": [
    "# NOTE: Make sure to change the server name to whatever is applicable in your environment. If running locally, then use only ak.connect().\n",
    "ak.connect(\"n32\", 5555)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3dd41e",
   "metadata": {},
   "source": [
    "### **Well-Connected Components**\n",
    "\n",
    "This algorithm performs steps to ensure that every cluster/community from a given graph and clustering inputs are well-connected. It returns the number of clusters found to be well-connected and outputs them to a file.\n",
    "\n",
    "Below are three examples that run all the variations of currently available well-connectedness criteria with three different output types. \n",
    "\n",
    "Any keyword arguments are optional, the default configurations can be found in the documentation string for the `well_connected_components` function. \n",
    "\n",
    "In the example below, `connectedness_criterion_mult_value` is always specified. However, this is only needed to be specified for when `connectedness_criterion` is equal to `mult`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "86b36041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/log10_test_network_wcc_output_post.tsv\n",
      "WCC found 66 clusters to be well-connected.\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/log2_test_network_wcc_output_post.tsv\n",
      "WCC found 35 clusters to be well-connected.\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/sqrt_test_network_wcc_output_post.tsv\n",
      "WCC found 66 clusters to be well-connected.\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/mult_test_network_wcc_output_post.tsv\n",
      "WCC found 65 clusters to be well-connected.\n"
     ]
    }
   ],
   "source": [
    "# Create an Arachne graph from a test network file.\n",
    "ar_network_graph = ar.read_tsv_file(os.path.abspath(\"./data/wcc/test_network.tsv\"))\n",
    "\n",
    "# Execute wcc with the absolute path to the generate and the create network file.\n",
    "file_path = os.path.abspath(\"./data/wcc/test_clustering.tsv\")\n",
    "output_path = os.path.abspath(\"./output/\")\n",
    "output_filename = \"_test_network_wcc_output_post.tsv\"\n",
    "\n",
    "criterion_variations = [\"log10\", \"log2\", \"sqrt\", \"mult\"]\n",
    "\n",
    "for variant in criterion_variations:\n",
    "    curr_output_filename = variant + output_filename\n",
    "    num_clusters = ar.well_connected_components(ar_network_graph, file_path, output_path, \n",
    "                                                output_filename=curr_output_filename,\n",
    "                                                output_type = \"post\",\n",
    "                                                connectedness_criterion = variant,\n",
    "                                                connectedness_criterion_mult_value = 0.05,\n",
    "                                                pre_filter_min_size=1, post_filter_min_size=1)\n",
    "    print(f\"WCC found {num_clusters} clusters to be well-connected.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f61f17d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/users/oaa9/arkouda-njit/arachne/outputlog10_test_network_wcc_output_during.tsv\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/log10_test_network_wcc_output_during.tsv\n",
      "WCC found 66 clusters to be well-connected.\n",
      "/scratch/users/oaa9/arkouda-njit/arachne/outputlog2_test_network_wcc_output_during.tsv\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/log2_test_network_wcc_output_during.tsv\n",
      "WCC found 35 clusters to be well-connected.\n",
      "/scratch/users/oaa9/arkouda-njit/arachne/outputsqrt_test_network_wcc_output_during.tsv\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/sqrt_test_network_wcc_output_during.tsv\n",
      "WCC found 66 clusters to be well-connected.\n",
      "/scratch/users/oaa9/arkouda-njit/arachne/outputmult_test_network_wcc_output_during.tsv\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/mult_test_network_wcc_output_during.tsv\n",
      "WCC found 65 clusters to be well-connected.\n"
     ]
    }
   ],
   "source": [
    "# Create an Arachne graph from a test network file.\n",
    "ar_network_graph = ar.read_tsv_file(os.path.abspath(\"./data/wcc/test_network.tsv\"))\n",
    "\n",
    "# Execute wcc with the absolute path to the generate and the create network file.\n",
    "file_path = os.path.abspath(\"./data/wcc/test_clustering.tsv\")\n",
    "output_path = os.path.abspath(\"./output/\")\n",
    "output_filename = \"_test_network_wcc_output_during.tsv\"\n",
    "\n",
    "criterion_variations = [\"log10\", \"log2\", \"sqrt\", \"mult\"]\n",
    "\n",
    "for variant in criterion_variations:\n",
    "    curr_output_filename = variant + output_filename\n",
    "\n",
    "    # NOTE: This is needed since Arachne errors out when output type is \"during\" and the file exists.\n",
    "    #       It errors out to prevent an already-existing file to be appended to.\n",
    "    print(output_path + curr_output_filename)\n",
    "    if os.path.exists(output_path + \"/\" + curr_output_filename):\n",
    "        os.remove(output_path + \"/\" + curr_output_filename)\n",
    "\n",
    "    num_clusters = ar.well_connected_components(ar_network_graph, file_path, output_path,\n",
    "                                                output_filename=curr_output_filename,\n",
    "                                                output_type = \"during\",\n",
    "                                                connectedness_criterion = variant,\n",
    "                                                connectedness_criterion_mult_value = 0.05,\n",
    "                                                pre_filter_min_size=1, post_filter_min_size=1)\n",
    "    print(f\"WCC found {num_clusters} clusters to be well-connected.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a3e0c47d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/log10_test_network_wcc_output_debug\n",
      "WCC found 66 clusters to be well-connected.\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/log2_test_network_wcc_output_debug\n",
      "WCC found 35 clusters to be well-connected.\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/sqrt_test_network_wcc_output_debug\n",
      "WCC found 66 clusters to be well-connected.\n",
      "Cluster files written to:  /scratch/users/oaa9/arkouda-njit/arachne/output/mult_test_network_wcc_output_debug\n",
      "WCC found 65 clusters to be well-connected.\n"
     ]
    }
   ],
   "source": [
    "# Create an Arachne graph from a test network file.\n",
    "ar_network_graph = ar.read_tsv_file(os.path.abspath(\"./data/wcc/test_network.tsv\"))\n",
    "\n",
    "# Execute wcc with the absolute path to the generate and the create network file.\n",
    "file_path = os.path.abspath(\"./data/wcc/test_clustering.tsv\")\n",
    "output_path = os.path.abspath(\"./output/\")\n",
    "output_filename = \"_test_network_wcc_output_debug\"\n",
    "\n",
    "criterion_variations = [\"log10\", \"log2\", \"sqrt\", \"mult\"]\n",
    "\n",
    "for variant in criterion_variations:\n",
    "    curr_output_filename = variant + output_filename\n",
    "    num_clusters = ar.well_connected_components(ar_network_graph, file_path, output_path,\n",
    "                                                output_filename=curr_output_filename,\n",
    "                                                output_type = \"debug\",\n",
    "                                                connectedness_criterion = variant,\n",
    "                                                connectedness_criterion_mult_value = 0.05,\n",
    "                                                pre_filter_min_size=1, post_filter_min_size=1)\n",
    "    print(f\"WCC found {num_clusters} clusters to be well-connected.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b988454",
   "metadata": {},
   "source": [
    "### **Connected Components**\n",
    "\n",
    "Given a graph G, (as defined above) if there is a path from a vertex $u$ to every other vertex $v$ then the graph is said to be connected. If there is not a path, then the graph is said to be disconnected and composed of multiple connected components. There may be a large number of connected components of varying sizes in a graph.\n",
    "\n",
    "One of the most ancient manners of calculating connected components involve running BFS until every vertex has been visited, at every iteration looking for the node whose value `-1` and start BFS from it. These steps are repeated until all the vertices have been labeled. The other is using union-find to build a tree induced by each connected component. For our method we use a lbel propagation technique that sends the minimum vertex label to all the other vertices in a connected component.\n",
    "\n",
    "To run our connected components, you just have to call the `connected_components()` method. **We use a randomly generated graph for this example**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3aca456",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/users/oaa9/arkouda/arkouda/alignment.py:248: UserWarning: Duplicate terms present in search space. Only first instance of each query term will be reported. To return all occurrences, set all_occurrences=True.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "rmat_graph = ar.rmat(15, create_using=ar.Graph)\n",
    "cc = ar.connected_components(rmat_graph)\n",
    "print(cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c2f6bc",
   "metadata": {},
   "source": [
    "We can use Arkouda methods to get the size of each component in our file. This will let us know which vertices will be included in the induced subgraph of the largest component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "474af8ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The largest component is labeled: 0\n",
      "The smallest component is labeled: 1896\n"
     ]
    }
   ],
   "source": [
    "g = ak.GroupBy(cc)\n",
    "keys, count = g.size()\n",
    "label_of_largest_component = ak.argmax(count)\n",
    "label_of_smallest_component = ak.argmin(count)\n",
    "print(f\"The largest component is labeled: {keys[label_of_largest_component]}\")\n",
    "print(f\"The smallest component is labeled: {keys[label_of_smallest_component]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2d6b6e",
   "metadata": {},
   "source": [
    "### **Graph Generation and Loading**\n",
    "Graphs can be built from existing data or generated with our suite of random graph generators. The preferred way to load a graph into memory is from Arkouda arrays, however we provide a method to read a graph in from matrix market format or randomly generate some graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b6a0f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in a graph from a matrix market file.\n",
    "absolute_path_to_karate = os.path.abspath(\"data/karate.mtx\")\n",
    "karate = ar.read_matrix_market_file(absolute_path_to_karate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b625a57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated graph has 24186 vertices and 441534 edges\n",
      "Generated graph has 100 vertices and 2610 edges\n",
      "Generated graph has 100 vertices and 100 edges\n",
      "Generated graph has 100 vertices and 2021 edges\n"
     ]
    }
   ],
   "source": [
    "# Generate a random graph using any variety of random generators available.\n",
    "n = 100\n",
    "rmat_graph = ar.rmat(15, create_using=ar.Graph)\n",
    "gnp_graph = ar.gnp(n, 0.75, create_using=ar.Graph)\n",
    "rtree_graph = ar.random_tree(n, create_using=ar.Graph)\n",
    "ws_graph = ar.watts_strogatz_graph(n, 25, 0.56, create_using=ar.Graph)\n",
    "graph_list = [rmat_graph, gnp_graph, rtree_graph, ws_graph]\n",
    "for g in graph_list:\n",
    "    print(f\"Generated graph has {len(g)} vertices and {g.size()} edges\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47d13bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a property graph from Arkouda dataframes, usually read in from HDF5, Parquet, or CSV files. For demonstrative purposes, we create some random dataset here.\n",
    "n = 1_000           # Number of vertices.\n",
    "m = 1_000_000       # Number of edges.\n",
    "k = 2               # Value to cap the randomness at."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5d0712b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create variously different random arrays of different types: integers, unsigned integers, floats, booleans, strings, and categoricals.\n",
    "src_array = ak.randint(0, n, m, dtype=ak.dtype('int64'), seed=2)\n",
    "dst_array = ak.randint(0, n, m, dtype=ak.dtype('int64'), seed=4)\n",
    "int_array = ak.randint(-1, k, m, dtype=ak.dtype('int64'), seed=6)\n",
    "uint_array = ak.randint(0, k, m, dtype=ak.dtype('uint64'), seed=8)\n",
    "real_array = ak.randint(0, k, m, dtype=ak.dtype('float64'), seed=10)\n",
    "bool_array = ak.randint(0, k, m, dtype=ak.dtype('bool'), seed=12)\n",
    "strings_array = ak.random_strings_uniform(0, k, m, characters=\"abcdefghijklmonpqrstuvwxyz\", seed=14)\n",
    "categorical_array = ak.Categorical(ak.random_strings_uniform(0, k, m, characters=\"abcdefghijklmonpqrstuvwxyz\", seed=14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c9b38b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize an empty graph object.\n",
    "prop_graph = ar.PropGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af0f2c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe with the edge data.\n",
    "test_edge_dict = {\n",
    "    \"src\":src_array,\n",
    "    \"dst\":dst_array,\n",
    "    \"data1\":int_array,\n",
    "    \"data2\":uint_array,\n",
    "    \"data3\":real_array,\n",
    "    \"data4\":bool_array,\n",
    "    \"data5\":strings_array,\n",
    "    \"data6\":categorical_array\n",
    "}\n",
    "test_edge_df = ak.DataFrame(test_edge_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0adda3be",
   "metadata": {},
   "outputs": [
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       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><p>1000000 rows x 8 columns</p>"
      ],
      "text/plain": [
       "        src  dst  data1  data2     data3  data4 data5 data6\n",
       "0       351  671      0      0  0.638154  False     B     B\n",
       "1       278   71      0      0  1.366654  False     L     L\n",
       "2       828  401      0      1  0.887981   True     N     N\n",
       "3       358  706     -1      0  1.219599  False            \n",
       "4       681  898      1      0  1.191131   True     Q     Q\n",
       "...     ...  ...    ...    ...       ...    ...   ...   ...\n",
       "999995  619  263      1      0  1.663987  False            \n",
       "999996  927  756     -1      0  0.781876   True     I     I\n",
       "999997   63  449      1      0  1.614944  False            \n",
       "999998  938  493      1      0  0.226886   True            \n",
       "999999  465  523      0      1  0.014557   True             (1000000 rows x 8 columns)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_edge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ca0bbe6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the edge attributes, with sorts the edges and handles storing their data.\n",
    "prop_graph.load_edge_attributes(test_edge_df, source_column=\"src\", destination_column=\"dst\", relationship_columns=[\"data5\", \"data1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e94bf24e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create sizes for vertex information.\n",
    "m = len(prop_graph)\n",
    "k = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5dc37703",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create data of different types for vertices.\n",
    "int_array = ak.randint(-1, k, m, dtype=ak.dtype('int64'), seed=6)\n",
    "uint_array = ak.randint(0, k, m, dtype=ak.dtype('uint64'), seed=8)\n",
    "real_array = ak.randint(0, k, m, dtype=ak.dtype('float64'), seed=10)\n",
    "bool_array = ak.randint(0, k, m, dtype=ak.dtype('bool'), seed=12)\n",
    "strings_array = ak.random_strings_uniform(0, k, m, characters=\"abcdefghijklmonpqrstuvwxyz\", seed=14)\n",
    "categorical_array = ak.Categorical(ak.random_strings_uniform(0, k, m, characters=\"abcdefghijklmonpqrstuvwxyz\", seed=14))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d849d4ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe with vertex data.\n",
    "test_node_dict = {\n",
    "    \"nodes\":prop_graph.nodes(),\n",
    "    \"data1\":int_array,\n",
    "    \"data2\":uint_array,\n",
    "    \"data3\":real_array,\n",
    "    \"data4\":bool_array,\n",
    "    \"data5\":strings_array,\n",
    "    \"data6\":categorical_array\n",
    "}\n",
    "test_node_df = ak.DataFrame(test_node_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "52c54e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nodes</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>data3</th>\n",
       "      <th>data4</th>\n",
       "      <th>data5</th>\n",
       "      <th>data6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.638154</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.366654</td>\n",
       "      <td>False</td>\n",
       "      <td>L</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.887981</td>\n",
       "      <td>True</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.219599</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.191131</td>\n",
       "      <td>True</td>\n",
       "      <td>Q</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>995</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.253123</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>996</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.924143</td>\n",
       "      <td>True</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.303909</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>998</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.732572</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.904719</td>\n",
       "      <td>True</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><p>1000 rows x 7 columns</p>"
      ],
      "text/plain": [
       "     nodes  data1  data2     data3  data4 data5 data6\n",
       "0        0      0      0  0.638154  False     B     B\n",
       "1        1      0      0  1.366654  False     L     L\n",
       "2        2      0      1  0.887981   True     N     N\n",
       "3        3     -1      0  1.219599  False            \n",
       "4        4      1      0  1.191131   True     Q     Q\n",
       "..     ...    ...    ...       ...    ...   ...   ...\n",
       "995    995      1      1  0.253123  False            \n",
       "996    996     -1      1  0.924143   True     B     B\n",
       "997    997      0      1  1.303909  False            \n",
       "998    998     -1      0  1.732572   True            \n",
       "999    999     -1      0  1.904719   True             (1000 rows x 7 columns)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_node_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "56581839",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the vertex data.\n",
    "prop_graph.load_node_attributes(test_node_df, node_column=\"nodes\", label_columns=[\"data5\", \"data2\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61f19115",
   "metadata": {},
   "source": [
    "### **Graph Processing and Querying**\n",
    "Treating the graphs as dataframes allows us to exploit Arkouda's array searches to generate subgraphs in seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2343803e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filters for vertices.\n",
    "def node_filter(node_attributes):\n",
    "    return node_attributes[\"data2\"] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "94b1b23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create filters for edges.\n",
    "def edge_filter(edge_attributes):\n",
    "    return edge_attributes[\"data1\"] > -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ce31b434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subgraph generated with edge size: 467134\n",
      "Subgraph generated with edge size: 420930\n",
      "Subgraph generated with edge size: 100891\n"
     ]
    }
   ],
   "source": [
    "# Create different subgraphs from each demo and together.\n",
    "subgraph_nodes = prop_graph.subgraph_view(filter_node=node_filter)\n",
    "print(f\"Subgraph generated with edge size: {subgraph_nodes.size()}\")\n",
    "subgraph_edges = prop_graph.subgraph_view(filter_edge=edge_filter)\n",
    "print(f\"Subgraph generated with edge size: {subgraph_edges.size()}\")\n",
    "subgraph_together = prop_graph.subgraph_view(filter_node=node_filter, filter_edge=edge_filter)\n",
    "print(f\"Subgraph generated with edge size: {subgraph_together.size()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eded8d6c",
   "metadata": {},
   "source": [
    "### **Graph Algorithms**\n",
    "Let's now revisit the karate graph from above and do some analyses with Arachne and NetworkX together! First, let's start out by reading the matrix market file again, both with Arachne and NetworkX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "61aa774c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, let's build the graph with Arachne.\n",
    "filepath = os.path.abspath(\"data/karate.mtx\")\n",
    "G = ar.read_matrix_market_file(filepath)\n",
    "\n",
    "edge_src, edge_dst = G.edges()\n",
    "edge_src = edge_src.to_list()\n",
    "edge_dst = edge_dst.to_list()\n",
    "edge_list = []\n",
    "for (u,v) in zip(edge_src,edge_dst):\n",
    "    edge_list.append((u,v))\n",
    "nodes = G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7378b0cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's make sure that both graphs have the same number of vertices and edges. Arachne graph has 34 vertices and 78 edges. NetworkX has 34 vertices and 78 edges.\n"
     ]
    }
   ],
   "source": [
    "# Secondly, let's build the graph with NetworkX. NOTE: Arachne has a direct read_matrix_market_file method whereas NetworkX requires you to use SciPy to read in the matrix market file.\n",
    "fh = open(filepath, \"rb\")\n",
    "H = nx.from_scipy_sparse_array(sp.io.mmread(fh))\n",
    "print(f\"Let's make sure that both graphs have the same number of vertices and edges. Arachne graph has {len(G)} vertices and {G.size()} edges. NetworkX has {len(H)} vertices and {H.size()} edges.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10dadb6a",
   "metadata": {},
   "source": [
    "We are also able to display our graph with NetworkX methods by exporting our edgelist and building a graph from the edges stored by Arachne. **Note: Visualization is resource-intensive, so you can really only do it for small graphs. However, we perform these steps to show how Arachne can complement NetworkX and how graphs can be exchanged between them.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "21ef43f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx_display = nx.Graph()\n",
    "nx_display.add_edges_from(edge_list)\n",
    "\n",
    "pos = nx.kamada_kawai_layout(nx_display)\n",
    "nx.draw_networkx(nx_display, pos, with_labels=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1cba34d",
   "metadata": {},
   "source": [
    "Below we also see how we can extract a degree view for a graph to see the dispersion of degrees amongst the vertices. This also works for directed graphs by using the `G.in_degree()` and `G.out_degree()` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "07067f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16 9 10 6 3 4 4 4 5 2 3 1 2 5 2 2 2 2 2 3 2 2 2 5 3 3 2 4 3 4 4 6 12 17]\n"
     ]
    }
   ],
   "source": [
    "degrees = G.degree()\n",
    "print(degrees)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94f7772e",
   "metadata": {},
   "source": [
    "Using Arkouda arrays we can also extract the node with maximum degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c33d3fac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node 34 has maximum degree of 17\n"
     ]
    }
   ],
   "source": [
    "print(f\"Node {G.nodes()[ak.argmax(degrees)]} has maximum degree of {ak.max(degrees)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b71fed3",
   "metadata": {},
   "source": [
    "### **Breadth-First Search**\n",
    "\n",
    "Given a graph $G$ (as defined above) and a source vertex $s$, breadth-first search (BFS) traverses the graph in a level-centric manner. It can return the tree inherently generated during BFS, the vertices found at each layer, or an ordering of nodes as predecessors or successors. For our implementation we opted to mimic NetworkX's `bfs_layers` function that returns the nodes and the layer they belong to.\n",
    "\n",
    "Our BFS method, written in Chapel, expands the next frontier in parallel on each locale, if it is run on a distributed cluster. Each locale iterates over the frontier, and if there are any local edges, it inspects the neighbors, and if not yet visited, updates the `depth` (layer) pdarray. The final `depth` array is returned which can be used to provide a different view of a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "34d4c13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 1 1 1 1 1 2 1 1 1 1 3 3 2 1 3 1 3 1 3 3 2 2 3 2 2 3 2 1 2 2]\n"
     ]
    }
   ],
   "source": [
    "d = ar.bfs_layers(G, 1)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1345792d",
   "metadata": {},
   "source": [
    "We may also extract a histogram of the sizes of each level computed by BFS. This can be a useful manner in seeing how how big each group of vertices at a particular level is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2bef7b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 16 9 8]\n"
     ]
    }
   ],
   "source": [
    "d_histogram = ak.histogram(d, bins=ak.max(d)+1)\n",
    "print(d_histogram[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b8924b9",
   "metadata": {},
   "source": [
    "From the above we can see that at level 0 we obviously only have one vertex. Then, we can see that at level 1 we have 16, level 2 we have 9, and level 3 we have 8. The maximum level is 3. What happens if we change the source vertex? Let's see the results of bfs_layers for each vertex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "32bc98b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of breadth-first search layers for vertex 1: [1 16 9 8]\n",
      "Size of breadth-first search layers for vertex 2: [1 9 13 11]\n",
      "Size of breadth-first search layers for vertex 3: [1 10 20 3]\n",
      "Size of breadth-first search layers for vertex 4: [1 6 16 11]\n",
      "Size of breadth-first search layers for vertex 5: [1 3 14 8 8]\n",
      "Size of breadth-first search layers for vertex 6: [1 4 13 8 8]\n",
      "Size of breadth-first search layers for vertex 7: [1 4 13 8 8]\n",
      "Size of breadth-first search layers for vertex 8: [1 4 17 11 1]\n",
      "Size of breadth-first search layers for vertex 9: [1 5 25 3]\n",
      "Size of breadth-first search layers for vertex 10: [1 2 20 10 1]\n",
      "Size of breadth-first search layers for vertex 11: [1 3 14 8 8]\n",
      "Size of breadth-first search layers for vertex 12: [1 1 15 9 8]\n",
      "Size of breadth-first search layers for vertex 13: [1 2 14 9 8]\n",
      "Size of breadth-first search layers for vertex 14: [1 5 25 3]\n",
      "Size of breadth-first search layers for vertex 15: [1 2 16 6 8 1]\n",
      "Size of breadth-first search layers for vertex 16: [1 2 16 6 8 1]\n",
      "Size of breadth-first search layers for vertex 17: [1 2 3 12 8 8]\n",
      "Size of breadth-first search layers for vertex 18: [1 2 15 8 8]\n",
      "Size of breadth-first search layers for vertex 19: [1 2 16 6 8 1]\n",
      "Size of breadth-first search layers for vertex 20: [1 3 27 3]\n",
      "Size of breadth-first search layers for vertex 21: [1 2 16 6 8 1]\n",
      "Size of breadth-first search layers for vertex 22: [1 2 15 8 8]\n",
      "Size of breadth-first search layers for vertex 23: [1 2 16 6 8 1]\n",
      "Size of breadth-first search layers for vertex 24: [1 5 15 4 8 1]\n",
      "Size of breadth-first search layers for vertex 25: [1 3 6 23 1]\n",
      "Size of breadth-first search layers for vertex 26: [1 3 6 23 1]\n",
      "Size of breadth-first search layers for vertex 27: [1 2 15 6 9 1]\n",
      "Size of breadth-first search layers for vertex 28: [1 4 20 8 1]\n",
      "Size of breadth-first search layers for vertex 29: [1 3 21 8 1]\n",
      "Size of breadth-first search layers for vertex 30: [1 4 15 5 8 1]\n",
      "Size of breadth-first search layers for vertex 31: [1 4 20 8 1]\n",
      "Size of breadth-first search layers for vertex 32: [1 6 26 1]\n",
      "Size of breadth-first search layers for vertex 33: [1 12 12 8 1]\n",
      "Size of breadth-first search layers for vertex 34: [1 17 6 9 1]\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,len(G)+1):\n",
    "    d = ar.bfs_layers(G, i)\n",
    "    d_histogram = ak.histogram(d, bins=ak.max(d)+1)\n",
    "    print(f\"Size of breadth-first search layers for vertex {i}: {d_histogram[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b13d9f2a",
   "metadata": {},
   "source": [
    "From the output above we notice some main things:\n",
    "1. Starting BFS from different sources arise to different number of layers for the output of each vertex.\n",
    "2. Some nodes have the same number of vertices at each layer. \n",
    "3. Vertex 19 provides the largest number of layers for any of the vertices. \n",
    "\n",
    "Let's color the nodes for node 19 and show the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c100625a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 2 5 4 0 2 2 4 2 2 3 3 2 2 2 2 2 2 1 1]\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 20, 22, 32, 31, 10, 28, 29, 33, 17, 34, 15, 16, 19, 21, 23, 24, 26, 30, 25, 27]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "degrees = [16 9 10 6 3 4 4 4 5 2 3 1 2 5 2 2 2 2 2 3 2 2 2 5 3 3 2 4 3 4 4 6 12 17]\n"
     ]
    }
   ],
   "source": [
    "d = ar.bfs_layers(G, 19)\n",
    "color_dict = {0:\"blue\", 1:\"green\", 2:\"red\", 3:\"purple\", 4:\"orange\", 5:\"yellow\"}\n",
    "print(d)\n",
    "\n",
    "color_map = []\n",
    "nodes = nx_display.nodes()\n",
    "for u in nodes:\n",
    "    color_map.append(color_dict[d[u-1]])\n",
    "    \n",
    "print(nx_display.nodes)\n",
    "\n",
    "pos = nx.kamada_kawai_layout(nx_display)\n",
    "nx.draw_networkx(nx_display, pos, with_labels=True, node_color=color_map)\n",
    "plt.show()\n",
    "\n",
    "print(f\"degrees = {degrees}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891c38f1",
   "metadata": {},
   "source": [
    "In the returned image we can see the first frontier are the green vertices $<0, 1, 33>$. If we look at the degree view of the graph we notice that 0, 1, and 33 are in the top 4 of vertices with most degree, which now makes sense why vertex 19 had the largest expanded frontier. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26d4aab",
   "metadata": {},
   "source": [
    "### **Triangle Counting**\n",
    "One of the most basic community structures that can be found in graphs are triangles. Triangles are 3-cliques in an undirected graph which means three nodes are strongly connected to each other. Triangle counting can be used to detect how cohesive communities are, the more triangles there are, the better connected a community is. It can also be used to drive other graph analytical algorithms such as centrality measures (triangle centrality) and substructure detection (k-truss analytics).\n",
    "\n",
    "Our triangle counting method involves inspecting every edge in a graph and the adjacency lists of both endpoints to find the intersection point. Most algorithms perform list intersection, for our case we perform a binary search of every vertex in the smaller adjacency list into the bigger one. This allows us to efficiently find triangles with less work.\n",
    "\n",
    "Please note that you have to divide the total number of triangles found by 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ef580d45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The whole graph has 45.0 triangles.\n"
     ]
    }
   ],
   "source": [
    "G_tri = ar.triangles(G)\n",
    "print(f\"The whole graph has {G_tri/3} triangles.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3119c65",
   "metadata": {},
   "source": [
    "You can also pass an array of vertex names to return only the number of triangles those vertices belong to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "922ec7c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vertex 1 has 21 triangles and vertex 19 has 1 triangles.\n"
     ]
    }
   ],
   "source": [
    "G_tris = ar.triangles(G, ak.array([1,19]))\n",
    "print(f\"Vertex 1 has {G_tris[0]} triangles and vertex 19 has {G_tris[1]} triangles.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7808a40",
   "metadata": {},
   "source": [
    "### **Truss Analytics**\n",
    "\n",
    "Given a graph $G$ (as defined above), the $k$-truss of a graph is one where every edge is a part of least $k-2$ triangles. This creates a cohesive subgraph where only the edges that meet these requirement are kept. It involves recalculating the number of triangles multiple times, which we avoid by tracking the support of each edge at every iteration of the algorithm. We provide novel algorithmic implementations for three truss analytical algorithms:\n",
    "1. $k$-truss\n",
    "2. max-truss\n",
    "3. truss decomposition\n",
    "\n",
    "Below we run `k_truss` and display the edges that belong to at least 2 triangles. We could build an induced subgraph from these edges in the same manner as we showed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "453d1389",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/users/oaa9/arkouda/arkouda/alignment.py:248: UserWarning: Duplicate terms present in search space. Only first instance of each query term will be reported. To return all occurrences, set all_occurrences=True.\n",
      "  warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1 -1 -1 3 3 3 -1 3 3 3 3 -1 3 3 3 3 -1 -1 -1 -1 3 3 3 3 -1 -1 3 3 -1 3 3 3 -1 3 -1 3 3 3 3 3 3 -1 -1 -1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 -1 -1 -1 3 3 3 3 3 3 3 3 3 -1 -1 -1 -1 3 3 -1]\n"
     ]
    }
   ],
   "source": [
    "kt = ar.k_truss(G, 4)\n",
    "print(kt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f569d48",
   "metadata": {},
   "source": [
    "Here, we see that the maximum truss of this graph is 5. This means that the $k$-truss is no longer defined for any value of $k$ larger than 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d0cf789b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "mt = ar.max_truss(G)\n",
    "print(mt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ebe66d5",
   "metadata": {},
   "source": [
    "Lastly, the truss decomposition shows the trussness of every edge. This means that the largest value of k for every edge is the one presented in the edge index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f9453589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 5 5 3 3 3 5 3 3 2 3 5 3 3 3 2 5 5 5 5 3 3 3 2 5 5 3 2 5 2 2 3 5 3 5 3 3 3 3 3 3 4 4 4 2 2 3 3 3 3 3 3 2 3 3 3 3 2 3 4 4 4 3 2 3 3 3 3 3 3 3 4 4 4 4 3 3 4]\n"
     ]
    }
   ],
   "source": [
    "td = ar.truss_decomposition(G)\n",
    "print(td)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada7b537",
   "metadata": {},
   "source": [
    "Using similar coloring steps as above, we can actually color the edges of the graph. The steps for that are below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c6bfe6fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_dict = {5:\"red\", 4:\"purple\", 3:\"green\", 2:\"blue\"}\n",
    "edge_color = []\n",
    "\n",
    "for i in range(G.size()):\n",
    "    edge_color.append(color_dict[td[i]])\n",
    "    \n",
    "pos = nx.spring_layout(nx_display, seed=200)\n",
    "nx.draw_networkx(nx_display, pos, with_labels=True, edge_color=edge_color)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f764afd",
   "metadata": {},
   "source": [
    "### **Triangle Centrality**\n",
    "\n",
    "Triangle centrality is a centrality measure to decide how important a vertex is based off how many triangles surround a particular vertex. It is calculated based off the following formula as seen in the paper titled \"Triangle Centrality\" by Paul Burkhardt.\n",
    "\n",
    "$$TC(v) = \\frac{\\frac{1}{3} \\sum_{u \\in N_{\\Delta}^{+}(v)}{\\Delta(u)} + \\sum_{w \\in (N(v) \\backslash N_{\\Delta}(v))} {\\Delta (w)}}{\\Delta(G)}$$\n",
    "\n",
    "Where $N(v)$ is the neighborhood set of a vertex $v$, $N_{\\Delta}(v)$ is the set of neighbors that are in triangles with $v$, and $N_{\\Delta}^{+}$ is the closed set that includes $v$. There is not an equivalent metric of this form available in NetworkX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4aa9be7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One of the most important vertices in the graph is: 14\n"
     ]
    }
   ],
   "source": [
    "tce = ar.triangle_centrality(G)\n",
    "print(f\"One of the most important vertices in the graph is: {G.nodes()[ak.argmax(tce)]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f3f8ea",
   "metadata": {},
   "source": [
    "### **Subgraph Isomorphism**\n",
    "\n",
    "The problem of motif finding in graphs is one of pattern matching with a smaller subgraph to search inside of a larger host graph. Here, we have an implementation of parallel VF2 that returns the subgraph mappings of vertices inside of the graph that contain the same structure as the given subgraph. Currently, our subgraph isomorphism method works only for property graphs. Further, it returns monomorphisms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7fd4d820",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_directed = ar.PropGraph()\n",
    "edges_to_add = G.edges()\n",
    "G_directed.add_edges_from(edges_to_add[0], edges_to_add[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "999dfc01",
   "metadata": {},
   "outputs": [],
   "source": [
    "subgraph = ar.PropGraph()\n",
    "src = [0, 1, 2, 1]\n",
    "dst = [1, 2, 0, 3]\n",
    "subgraph.add_edges_from(ak.array(src), ak.array(dst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "36d90c88",
   "metadata": {},
   "outputs": [],
   "source": [
    "isos = ar.subgraph_isomorphism(G_directed, subgraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "db263405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We found 1848.0 triangles with tails inside of the karate graph\n"
     ]
    }
   ],
   "source": [
    "print(f\"We found {len(isos)/4} triangles with tails inside of the karate graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a27650",
   "metadata": {},
   "source": [
    "### **Diameter**\n",
    "\n",
    "The diameter of a graph is the longest of the shortest paths between two vertices in a graph. Here, we show an experimental version that approximates the diameter with connected components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "60bb8a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The diameter of karate is 4\n"
     ]
    }
   ],
   "source": [
    "di = ar.diameter(G)\n",
    "print(f\"The diameter of karate is {di}\") "
   ]
  }
 ],
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