demos/more_examples/graphistry_features/hop_and_chain_graph_pattern_mining.ipynb

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      "source": [
        "# Hop-and-chain: PyGraphistry Cypher-style graph pattern matching on dataframes\n",
        "\n",
        "PyGraphistry supports a rich subset of the popular Cypher graph query language, which you can run on dataframes without needing to install a database nor native libraries. It is natively integrated with dataframes and thus has a Python-native syntax rather than the traditional string syntax.\n",
        "\n",
        "**PyGraphistry graph pattern matching features key similarities with Cypher**\n",
        "\n",
        "* Multi-hop searching\n",
        "* Predicates on node and edge attributes\n",
        "* Ability to identify matching nodes and edges\n",
        "\n",
        "**It is different in a few key ways**\n",
        "\n",
        "* Pure PyData (Python/C++/Fortran): No need to install databases, Java, etc., `pip install pygraphistry` is enough\n",
        "* It is collection-oriented rather than path-oriented: All operations are guaranteed to translate to efficiently vectorized dataframe operations rather than asymptotically slower per-row path operations typical of traditional graph query engines\n",
        "* Advanced users can insert custom predicates as native Python dataframe code\n",
        "\n",
        "---\n",
        "\n",
        "# Tutorial:\n",
        "\n",
        "1. Install & configure\n",
        "1. Load & enrich a US congress twitter interaction dataset\n",
        "1. Simple graph filtering: `g.hop()`  and `g.chain([...])`\n",
        "1. Multi-hop and paths-between-nodes pattern mining\n",
        "1. Advanced filter predicates\n",
        "1. Result labeling\n",
        "\n"
      ],
      "metadata": {
        "id": "GZxoiU8sQDk_"
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    {
      "cell_type": "markdown",
      "source": [
        "# 1. Install & configure"
      ],
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        "id": "QQpsrtwBT7sa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#! pip install graphistry[igraph]"
      ],
      "metadata": {
        "id": "cYjRbgkU9Sx8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "Ff6Tt9DhkePl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "import graphistry\n",
        "\n",
        "from graphistry import (\n",
        "\n",
        "    # graph operators\n",
        "    n, e_undirected, e_forward, e_reverse,\n",
        "\n",
        "    # attribute predicates\n",
        "    is_in, ge, startswith, contains, match as match_re\n",
        ")"
      ],
      "metadata": {
        "id": "S5_y0CbLkjft"
      },
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graphistry.register(api=3, username='...', password='...')"
      ],
      "metadata": {
        "id": "GQ83i-sKUaw9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Load & enrich a US congress twitter interaction dataset"
      ],
      "metadata": {
        "id": "eU9SyauNUHtR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data\n",
        "\n",
        "* Download\n",
        "* Turn json into a Pandas edges dataframe\n",
        "* Turn edges dataframe into a PyGraphistry graph\n",
        "* Enrich nodes and edges with some useful graph metrics\n",
        "* Visualize full graph to test"
      ],
      "metadata": {
        "id": "AM9JhnaQkRd3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! wget -q https://snap.stanford.edu/data/congress_network.zip\n",
        "! unzip congress_network.zip\n"
      ],
      "metadata": {
        "id": "55xeNAyDXhAm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "287758f0-0df2-49ff-ecdc-283313f7e07a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 1.2M\n",
            "drwxr-xr-x 1 root root 4.0K Dec  4 03:56 .\n",
            "drwxr-xr-x 1 root root 4.0K Dec  4 03:33 ..\n",
            "-rw-r--r-- 1 root root 150K May  9  2017 Attribute\n",
            "-rw-r--r-- 1 root root  14K May  9  2017 Class_info\n",
            "drwxr-xr-x 4 root root 4.0K Nov 30 14:24 .config\n",
            "-rw-r--r-- 1 root root 190K Aug  5 05:26 congress_network.zip\n",
            "-rw-r--r-- 1 root root 320K May  9  2017 edgelist\n",
            "drwxr-xr-x 1 root root 4.0K Nov 30 14:27 sample_data\n",
            "-rw-r--r-- 1 root root   16 May  9  2017 Statistics\n",
            "-rw-r--r-- 1 root root 221K Dec  4 03:53 twitter.zip\n",
            "-rw-r--r-- 1 root root 299K May  9  2017 vertex2aid\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "with open('congress_network/congress_network_data.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "edges = []\n",
        "for i, name in enumerate(data[0]['usernameList']):\n",
        "  for ii, j in enumerate(data[0]['outList'][i]):\n",
        "    edges.append({\n",
        "        'from': name,\n",
        "        'to': data[0]['usernameList'][j],\n",
        "        'weight': data[0]['outWeight'][i][ii]\n",
        "    })\n",
        "edges_df = pd.DataFrame(edges)\n",
        "\n",
        "print(edges_df.shape)\n",
        "edges_df.sample(5)"
      ],
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          "height": 224
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        "outputId": "61a1a4cf-dfe1-4260-a427-46009f4e4aaf"
      },
      "execution_count": 40,
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        {
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          "text": [
            "(13289, 3)\n"
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          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load dataframe as a PyGraphistry graph\n",
        "\n",
        "Turn into a graph and precompute some useful graph metrics\n",
        "\n",
        "Recall that a `g` object, underneath, is essentially just two dataframes, `g._edges` and `g._nodes`, and with many useful graph methods:"
      ],
      "metadata": {
        "id": "XLFTgDTEDSeA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shape\n",
        "g = graphistry.edges(edges_df, 'from', 'to')\n",
        "\n",
        "# Enrich & style\n",
        "# Tip: Switch from compute_igraph to compute_cugraph when GPUs are available\n",
        "g2 = (g\n",
        "      .materialize_nodes()\n",
        "      .nodes(lambda g: g._nodes.assign(title=g._nodes.id))\n",
        "      .edges(lambda g: g._edges.assign(weight2=g._edges.weight))\n",
        "      .bind(point_title='title')\n",
        "      .compute_igraph('community_infomap')\n",
        "      .compute_igraph('pagerank')\n",
        "      .get_degrees()\n",
        "      .encode_point_color(\n",
        "          'community_infomap',\n",
        "          as_categorical=True,\n",
        "          categorical_mapping={\n",
        "              0: '#32a9a2', # vibrant teal\n",
        "              1: '#ff6b6b', # soft coral\n",
        "              2: '#f9d342', # muted yellow\n",
        "          }\n",
        "      )\n",
        ")\n",
        "\n",
        "g2._nodes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "aB1U7e0HXmHh",
        "outputId": "53b9fa91-0caf-4866-c5a9-d9cf80e3c9ac"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:edge index g._edge not set so using edge index as ID; set g._edge via g.edges(), or change merge_if_existing to FalseWARNING:root:edge index g._edge __edge_index__ missing as attribute in ig; using ig edge order for IDsWARNING:root:edge index g._edge not set so using edge index as ID; set g._edge via g.edges(), or change merge_if_existing to FalseWARNING:root:edge index g._edge __edge_index__ missing as attribute in ig; using ig edge order for IDs"
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            "text/plain": [
              "                  id            title  community_infomap  pagerank  degree_in  \\\n",
              "0     SenatorBaldwin   SenatorBaldwin                  0  0.001422         26   \n",
              "1    SenJohnBarrasso  SenJohnBarrasso                  0  0.001179         22   \n",
              "2      SenatorBennet    SenatorBennet                  0  0.001995         33   \n",
              "3    MarshaBlackburn  MarshaBlackburn                  0  0.001331         18   \n",
              "4      SenBlumenthal    SenBlumenthal                  0  0.001672         30   \n",
              "..               ...              ...                ...       ...        ...   \n",
              "470     RepJoeWilson     RepJoeWilson                  1  0.001780         21   \n",
              "471       RobWittman       RobWittman                  1  0.001017         13   \n",
              "472  rep_stevewomack  rep_stevewomack                  1  0.002637         35   \n",
              "473   RepJohnYarmuth   RepJohnYarmuth                  2  0.000555          5   \n",
              "474     RepLeeZeldin     RepLeeZeldin                  1  0.000511          3   \n",
              "\n",
              "     degree_out  degree  \n",
              "0            20      46  \n",
              "1            19      41  \n",
              "2            22      55  \n",
              "3            38      56  \n",
              "4            35      65  \n",
              "..          ...     ...  \n",
              "470          38      59  \n",
              "471          19      32  \n",
              "472          19      54  \n",
              "473          20      25  \n",
              "474          25      28  \n",
              "\n",
              "[475 rows x 7 columns]"
            ],
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              "\n",
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              "\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>id</th>\n",
              "      <th>title</th>\n",
              "      <th>community_infomap</th>\n",
              "      <th>pagerank</th>\n",
              "      <th>degree_in</th>\n",
              "      <th>degree_out</th>\n",
              "      <th>degree</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SenatorBaldwin</td>\n",
              "      <td>SenatorBaldwin</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001422</td>\n",
              "      <td>26</td>\n",
              "      <td>20</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SenJohnBarrasso</td>\n",
              "      <td>SenJohnBarrasso</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001179</td>\n",
              "      <td>22</td>\n",
              "      <td>19</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SenatorBennet</td>\n",
              "      <td>SenatorBennet</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001995</td>\n",
              "      <td>33</td>\n",
              "      <td>22</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MarshaBlackburn</td>\n",
              "      <td>MarshaBlackburn</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001331</td>\n",
              "      <td>18</td>\n",
              "      <td>38</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SenBlumenthal</td>\n",
              "      <td>SenBlumenthal</td>\n",
              "      <td>0</td>\n",
              "      <td>0.001672</td>\n",
              "      <td>30</td>\n",
              "      <td>35</td>\n",
              "      <td>65</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>470</th>\n",
              "      <td>RepJoeWilson</td>\n",
              "      <td>RepJoeWilson</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001780</td>\n",
              "      <td>21</td>\n",
              "      <td>38</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471</th>\n",
              "      <td>RobWittman</td>\n",
              "      <td>RobWittman</td>\n",
              "      <td>1</td>\n",
              "      <td>0.001017</td>\n",
              "      <td>13</td>\n",
              "      <td>19</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>472</th>\n",
              "      <td>rep_stevewomack</td>\n",
              "      <td>rep_stevewomack</td>\n",
              "      <td>1</td>\n",
              "      <td>0.002637</td>\n",
              "      <td>35</td>\n",
              "      <td>19</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>RepJohnYarmuth</td>\n",
              "      <td>RepJohnYarmuth</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000555</td>\n",
              "      <td>5</td>\n",
              "      <td>20</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>RepLeeZeldin</td>\n",
              "      <td>RepLeeZeldin</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000511</td>\n",
              "      <td>3</td>\n",
              "      <td>25</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>475 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    </button>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bd9ed6f8-6bfc-435b-8fbc-05e870649c0e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
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              "</div>\n",
              "    </div>\n",
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          "metadata": {},
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        "g2.plot()"
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          "height": 543
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        "outputId": "5b4b277e-17fd-4201-9518-25168b927c6f"
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      "execution_count": 79,
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Simple filtering: `g.hop()` & `g.chain([...])`\n",
        "\n",
        "We can filter by nodes, edges, and combinations of them\n",
        "\n",
        "The result is a graph where we can inspect the node and edge tables, or perform further graph operations, like visualization or further searches\n",
        "\n",
        "**Key concepts**\n",
        "\n",
        "There are 2 key methods:\n",
        "* `g.hop(...)`: filter triples of source node, edge, destination node\n",
        "* `g.chain([....])`: arbitrarily long sequence of node and edge predicates\n",
        "\n",
        "They reuse column operations core to dataframe libraries, such as comparison operators on strings, numbers, and dates\n",
        "\n",
        "**Sample tasks**\n",
        "\n",
        "This section shows how to:\n",
        "\n",
        "* Find SenSchumer and his immediate community (infomap metric)\n",
        "* Look at his entire community\n",
        "* Find everyone with high edge weight from/to SenSchumer; 2 hops either direction\n",
        "* Find everyone in his community\n",
        "\n"
      ],
      "metadata": {
        "id": "2sB-Mi7qkrM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g2.chain([n({'title': 'SenSchumer'})])._nodes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "ydS6ZUiUqx8E",
        "outputId": "825b0c85-e3c9-4453-abed-1b252cf804d1"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           id       title  community_infomap  pagerank  degree_in  degree_out  \\\n",
              "0  SenSchumer  SenSchumer                  2  0.001296         25          97   \n",
              "\n",
              "   degree  \n",
              "0     122  "
            ],
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-afb0aacc-ae94-448d-9af1-4404ddc87973');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also pass `chain()` a sequence of node and edge expressions"
      ],
      "metadata": {
        "id": "5vGMFFveUyLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g_immediate_community2 = g2.chain([n({'title': 'SenSchumer'}), e_undirected(), n({'community_infomap': 2})])\n",
        "\n",
        "print(len(g_immediate_community2._nodes), 'senators', len(g_immediate_community2._edges), 'relns')\n",
        "g_immediate_community2._edges[['from', 'to', 'weight2']].sort_values(by=['weight2']).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "I1L0ZRTrF_HH",
        "outputId": "9ded72c2-694b-40a4-f55a-ee86b51f290d"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "58 senators 69 relns\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          from              to   weight2\n",
              "22  SenSchumer  JacksonLeeTX18  0.001546\n",
              "46  SenSchumer     RepSarbanes  0.001546\n",
              "23  SenSchumer      RepJayapal  0.001546\n",
              "53  SenSchumer      PeterWelch  0.001546\n",
              "25  SenSchumer    RepDaveJoyce  0.001546\n",
              "26  SenSchumer   RepRobinKelly  0.001546\n",
              "28  SenSchumer    RepAndyKimNJ  0.001546\n",
              "29  SenSchumer   RepBarbaraLee  0.001546\n",
              "50  SenSchumer    RepPaulTonko  0.001546\n",
              "32  SenSchumer       RepMeijer  0.001546"
            ],
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              "  </style>\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-aa5c2c2c-15f5-4c1f-8ac9-1d5d56c01ef1');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-92558e5b-c40d-4f0e-bbc5-0bd4767590d6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g_immediate_community2.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "3oR6PhBAGVdX",
        "outputId": "3a2e4fa7-eb73-4a16-efa2-eb5b43bd929f"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
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              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Often, we are just filtering on a src node / edge / dst node triple, so `hop()` is a short-form for this. All the `hop()` parameters can also be passed to edge expressions as well."
      ],
      "metadata": {
        "id": "hRWJkIFAU5t_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g_community2 = g2.hop(source_node_match={'community_infomap': 2}, destination_node_match={'community_infomap': 2})\n",
        "\n",
        "print(len(g_community2._nodes), 'senators', len(g_community2._edges), 'relns')\n",
        "g_community2._edges.sort_values(by=['weight2']).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "5T5UWt8kFPuZ",
        "outputId": "fd709a7d-a697-40d5-c34b-354eea84b72f"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "214 senators 4993 relns\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "            from               to    weight   weight2\n",
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            ],
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              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349</th>\n",
              "      <td>RepDonBeyer</td>\n",
              "      <td>RepBeatty</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>RepDonBeyer</td>\n",
              "      <td>RepGaramendi</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>RepDonBeyer</td>\n",
              "      <td>RepChuyGarcia</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>RepDonBeyer</td>\n",
              "      <td>RepRaulGrijalva</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365</th>\n",
              "      <td>RepDonBeyer</td>\n",
              "      <td>USRepKeating</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>366</th>\n",
              "      <td>RepDonBeyer</td>\n",
              "      <td>RepRickLarsen</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>0.000658</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5cd5a28-7ec3-4551-9ec8-cfe8c7a3c1c5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d5cd5a28-7ec3-4551-9ec8-cfe8c7a3c1c5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d5cd5a28-7ec3-4551-9ec8-cfe8c7a3c1c5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0251ef57-0ccd-4d28-8f5a-0cbf8ad87e0a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0251ef57-0ccd-4d28-8f5a-0cbf8ad87e0a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0251ef57-0ccd-4d28-8f5a-0cbf8ad87e0a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g_community2.encode_point_color('pagerank', ['blue', 'yellow', 'red'], as_continuous=True).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "uMnOYQypG-KH",
        "outputId": "a498472b-3fe5-4e1c-f9c5-b7b45515b63f"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"d3e20a53-8e92-4722-b85a-41716fcd3787\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=18793994b1ac486fa1a0427c7281c94e&type=arrow&viztoken=4c7a064d-b59d-4884-a6fe-9c2f2ba7cc44&usertag=101e0a53-pygraphistry-0.29.7+20.g5b3795c&splashAfter=1701665066&info=true\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#d3e20a53-8e92-4722-b85a-41716fcd3787\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Multi-hop and paths-between-nodes pattern mining\n",
        "\n",
        "Method `chain([...])` can be used for looking more than one hop out, and even finding paths between nodes."
      ],
      "metadata": {
        "id": "o6yJ48oxKPiz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ex: All people bridging SenSchumer and SpeakerPelosi"
      ],
      "metadata": {
        "id": "fT4y7fH4KzAr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g_shumer_pelosi_bridges = g2.chain([\n",
        "    n({'title': 'SenSchumer'}),\n",
        "    e_undirected(),\n",
        "    n(),\n",
        "    e_undirected(),\n",
        "    n({'title': 'SpeakerPelosi'})\n",
        "])\n",
        "\n",
        "print(len(g_shumer_pelosi_bridges._nodes), 'senators')\n",
        "g_shumer_pelosi_bridges._edges.sort_values(by='weight').head(5)"
      ],
      "metadata": {
        "id": "tItpDvqCBjvC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "5f701cb9-8e0a-4495-bfbd-baff50466ae9"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66 senators\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          from              to    weight   weight2\n",
              "86  RepJayapal   SpeakerPelosi  0.000871  0.000871\n",
              "47  SenSchumer       RepMeijer  0.001546  0.001546\n",
              "23  SenSchumer  RepBuddyCarter  0.001546  0.001546\n",
              "24  SenSchumer      RepJudyChu  0.001546  0.001546\n",
              "26  SenSchumer      repcleaver  0.001546  0.001546"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d2d4ab4-7aa0-4265-b838-f9accc88bee3\" class=\"colab-df-container\">\n",
              "    <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>from</th>\n",
              "      <th>to</th>\n",
              "      <th>weight</th>\n",
              "      <th>weight2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>RepJayapal</td>\n",
              "      <td>SpeakerPelosi</td>\n",
              "      <td>0.000871</td>\n",
              "      <td>0.000871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>SenSchumer</td>\n",
              "      <td>RepMeijer</td>\n",
              "      <td>0.001546</td>\n",
              "      <td>0.001546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>SenSchumer</td>\n",
              "      <td>RepBuddyCarter</td>\n",
              "      <td>0.001546</td>\n",
              "      <td>0.001546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>SenSchumer</td>\n",
              "      <td>RepJudyChu</td>\n",
              "      <td>0.001546</td>\n",
              "      <td>0.001546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>SenSchumer</td>\n",
              "      <td>repcleaver</td>\n",
              "      <td>0.001546</td>\n",
              "      <td>0.001546</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d2d4ab4-7aa0-4265-b838-f9accc88bee3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4d2d4ab4-7aa0-4265-b838-f9accc88bee3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4d2d4ab4-7aa0-4265-b838-f9accc88bee3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-363729c8-c4d0-4ce5-ad07-7065be5eb0aa\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-363729c8-c4d0-4ce5-ad07-7065be5eb0aa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-363729c8-c4d0-4ce5-ad07-7065be5eb0aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g_shumer_pelosi_bridges.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "L_YmDjeRJzcw",
        "outputId": "c1abf11a-e48c-43ce-8087-a56052f3097c"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"6a78cfb6-e44b-4575-ba2d-af843b49f5b2\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=8f2663e9edb34f1b94d5e4caca692c08&type=arrow&viztoken=757f43a6-204a-47d4-8823-4ae619811627&usertag=101e0a53-pygraphistry-0.29.7+20.g5b3795c&splashAfter=1701665329&info=true\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#6a78cfb6-e44b-4575-ba2d-af843b49f5b2\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Advanced filter predicates\n",
        "\n",
        "We can use a variety of predicates for filtering nodes and edges beyond attribute value equality.\n",
        "\n",
        "Common tasks include comparing attributes using:\n",
        "* Set inclusion: `is_in([...])`\n",
        "* Numeric comparisons: `gt(...)`, `lt(...)`, `ge(...)`, `le(...)`\n",
        "* String comparison: `startswith(...)`, `endswith(...)`, `contains(...)`\n",
        "* Regular expression matching: `matches(...)`\n",
        "* Duplicate checking: `duplicated()`"
      ],
      "metadata": {
        "id": "dbRgj3qxLU5I"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graph where nodes are in the top 20 pagerank:"
      ],
      "metadata": {
        "id": "z69kYi3wMMHK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_20_pr = g2._nodes.pagerank.sort_values(ascending=False, ignore_index=True)[19]\n",
        "top_20_pr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHkjtTw-MVuA",
        "outputId": "d377eef1-8a2b-484a-b190-e95491eef4c2"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.005888600097034367"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g_high_pr = g2.chain([\n",
        "    n({'pagerank': ge(top_20_pr)}),\n",
        "    e_undirected(),\n",
        "    n({'pagerank': ge(top_20_pr)}),\n",
        "])\n",
        "\n",
        "len(g_high_pr._nodes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wDg9pUyJMR3V",
        "outputId": "7ba923cd-5faa-431e-8f8f-8da223a28a39"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g_high_pr.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "FVc-Mou-M6TO",
        "outputId": "68ea6daa-b75b-46ad-cc54-010512aab919"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"d6c42b6d-7293-4a2b-97d9-f77ef37704f3\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=878257be34124d768e6ba803f8150d7b&type=arrow&viztoken=ab212b71-e7d3-4688-a7e8-719992daf99f&usertag=101e0a53-pygraphistry-0.29.7+20.g5b3795c&splashAfter=1701666408&info=true\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#d6c42b6d-7293-4a2b-97d9-f77ef37704f3\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graph where the name includes Leader"
      ],
      "metadata": {
        "id": "MDHyapjHOY-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g_leaders = g2.hop(\n",
        "    source_node_match={'title': contains('Leader')},\n",
        "    destination_node_match = {'title': contains('Leader')}\n",
        ")\n",
        "\n",
        "print(len(g_leaders._nodes), 'leaders')\n",
        "\n",
        "g_leaders.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "mwkKlqhoO29-",
        "outputId": "9f3329f1-08f5-4d8f-c203-c049e59a101a"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 leaders\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"2888c33c-d366-4f48-942e-7db6fa67b814\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=625a68e102a24e6297e2eb77a3a244f5&type=arrow&viztoken=a7890c9c-5c2f-4a14-bf3d-c40d1402147c&usertag=101e0a53-pygraphistry-0.29.7+20.g5b3795c&splashAfter=1701666652&info=true\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#2888c33c-d366-4f48-942e-7db6fa67b814\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Graph of leaders and senators"
      ],
      "metadata": {
        "id": "g--y65A0PHNJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g_leaders_and_senators = g2.hop(\n",
        "    source_node_match={'title': match_re(r'Sen|Leader')},\n",
        "    destination_node_match = {'title': match_re(r'Sen|Leader')}\n",
        ")\n",
        "\n",
        "print(len(g_leaders_and_senators._nodes), 'leaders and senators')\n",
        "\n",
        "g_leaders_and_senators.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "mBfeLkA_Ol2i",
        "outputId": "e0353e9e-34c2-4fc5-a756-b0a9260e1edb"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "67 leaders and senators\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"4610a102-e7e3-4ba6-8d6e-48f29e6bde98\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=a8a25eef82714c47a75d239101046e82&type=arrow&viztoken=edc1d590-ca20-4abd-a3e3-077d482ab47a&usertag=101e0a53-pygraphistry-0.29.7+20.g5b3795c&splashAfter=1701666806&info=true\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#4610a102-e7e3-4ba6-8d6e-48f29e6bde98\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Result labeling\n",
        "\n",
        "It can be useful to name node and edges within the path query for downstream reasoning:"
      ],
      "metadata": {
        "id": "DuxWkZFVV9-R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g_bridges2 = g2.chain([\n",
        "    n({'title': 'SenSchumer'}),\n",
        "    e_undirected(name='from_schumer'),\n",
        "    n(name='found_bridge'),\n",
        "    e_undirected(name='from_pelosi'),\n",
        "    n({'title': 'SpeakerPelosi'})\n",
        "])\n",
        "\n",
        "print(len(g_bridges2._nodes), 'senators in full graph')\n",
        "\n",
        "named = g_bridges2._nodes[ g_bridges2._nodes.found_bridge ]\n",
        "print(len(named), 'bridging senators')\n",
        "edges = g_bridges2._edges\n",
        "print(len(edges[edges.from_schumer]), 'relns from_schumer', len(edges[edges.from_pelosi]), 'relns from_pelosi')\n",
        "\n",
        "g_bridges2.encode_point_color(\n",
        "    'found_bridge',\n",
        "    as_categorical=True,\n",
        "    categorical_mapping={\n",
        "        True: 'orange',\n",
        "        False: 'silver'\n",
        "    }\n",
        ").plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "6G4nnclNPpY8",
        "outputId": "5139bef8-45ba-4ae9-cadb-9da856eb6bc8"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "66 senators in full graph\n",
            "64 bridging senators\n",
            "75 relns from_schumer 83 relns from_pelosi\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "            <iframe id=\"3109a430-d1d7-4772-ba1a-6b9e5554c33a\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=92919f78f88e41eb8de850f1a959e088&type=arrow&viztoken=b849ca80-2af8-459b-b647-2afa9d1ce53e&usertag=101e0a53-pygraphistry-0.29.7+20.g5b3795c&splashAfter=1701668918&info=true\"\n",
              "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
              "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
              "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
              "                    \n",
              "            >\n",
              "            </iframe>\n",
              "        \n",
              "            <script>\n",
              "                try {\n",
              "                  $(\"#3109a430-d1d7-4772-ba1a-6b9e5554c33a\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
              "                } catch (e) { console.error('exn catching scroll', e); }\n",
              "            </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w3w4RRYkWXKo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}