stellargraph/stellargraph

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Interpreting nodes and edges with saliency maps in GCN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/interpretability/gcn-node-link-importance.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/interpretability/gcn-node-link-importance.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2",
   "metadata": {},
   "source": [
    "This demo shows how to use integrated gradients in graph convolutional networks to obtain accurate importance estimations for both the nodes and edges. The notebook consists of three parts:\n",
    "- setting up the node classification problem for Cora citation network\n",
    "- training and evaluating a GCN model for node classification\n",
    "- calculating node and edge importances for model's predictions of query (\"target\") nodes\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Axiomatic Attribution for Deep Networks. M. Sundararajan, A. Taly, and Q. Yan.\n",
    "    Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017\n",
    "    ([link](https://arxiv.org/pdf/1703.01365.pdf)).\n",
    "    \n",
    "[2] Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. arXiv: 1903.01610 ([link](https://arxiv.org/abs/1903.01610))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.3.0b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.3.0b\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.3.0b, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "import time\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, regularizers\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Loading the CORA network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8",
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, subjects = dataset.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_subjects, test_subjects = model_selection.train_test_split(\n",
    "    subjects, train_size=140, test_size=None, stratify=subjects\n",
    ")\n",
    "val_subjects, test_subjects = model_selection.train_test_split(\n",
    "    test_subjects, train_size=500, test_size=None, stratify=test_subjects\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "14",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_subjects)\n",
    "val_targets = target_encoding.transform(val_subjects)\n",
    "test_targets = target_encoding.transform(test_subjects)\n",
    "\n",
    "all_targets = target_encoding.transform(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15",
   "metadata": {},
   "source": [
    "### Creating the GCN model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since GCN is a full-batch model, we use the `FullBatchNodeGenerator` class.\n",
    "\n",
    "Note: For interpretability we require a dense matrix so we set `sparse=False` in the `FullBatchNodeGenerator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GCN (local pooling) filters...\n"
     ]
    }
   ],
   "source": [
    "generator = FullBatchNodeGenerator(G, sparse=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "19",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_subjects.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model: tn this example we use two GCN layers with 16-dimensional hidden node features at each layer with ELU activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21",
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [16, 16]\n",
    "gcn = GCN(\n",
    "    layer_sizes=layer_sizes,\n",
    "    activations=[\"elu\", \"elu\"],\n",
    "    generator=generator,\n",
    "    dropout=0.3,\n",
    "    kernel_regularizer=regularizers.l2(5e-4),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expose the input and output tensors of the GCN model for node prediction, via GCN.in_out_tensors() method:\n",
    "x_inp, x_out = gcn.in_out_tensors()\n",
    "# Snap the final estimator layer to x_out\n",
    "x_out = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `x_out` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=x_out)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),  # decay=0.001),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[metrics.categorical_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "27",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_subjects.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 - 0s - loss: 1.9493 - categorical_accuracy: 0.1429 - val_loss: 1.8287 - val_categorical_accuracy: 0.3040\n",
      "Epoch 2/20\n",
      "1/1 - 0s - loss: 1.7875 - categorical_accuracy: 0.3214 - val_loss: 1.7499 - val_categorical_accuracy: 0.3040\n",
      "Epoch 3/20\n",
      "1/1 - 0s - loss: 1.6701 - categorical_accuracy: 0.3357 - val_loss: 1.6854 - val_categorical_accuracy: 0.3040\n",
      "Epoch 4/20\n",
      "1/1 - 0s - loss: 1.5617 - categorical_accuracy: 0.3286 - val_loss: 1.6155 - val_categorical_accuracy: 0.3160\n",
      "Epoch 5/20\n",
      "1/1 - 0s - loss: 1.4499 - categorical_accuracy: 0.3786 - val_loss: 1.5322 - val_categorical_accuracy: 0.3680\n",
      "Epoch 6/20\n",
      "1/1 - 0s - loss: 1.3278 - categorical_accuracy: 0.5071 - val_loss: 1.4399 - val_categorical_accuracy: 0.4700\n",
      "Epoch 7/20\n",
      "1/1 - 0s - loss: 1.1788 - categorical_accuracy: 0.6214 - val_loss: 1.3484 - val_categorical_accuracy: 0.5820\n",
      "Epoch 8/20\n",
      "1/1 - 0s - loss: 1.0673 - categorical_accuracy: 0.7571 - val_loss: 1.2649 - val_categorical_accuracy: 0.6440\n",
      "Epoch 9/20\n",
      "1/1 - 0s - loss: 0.9381 - categorical_accuracy: 0.8357 - val_loss: 1.1877 - val_categorical_accuracy: 0.6900\n",
      "Epoch 10/20\n",
      "1/1 - 0s - loss: 0.8570 - categorical_accuracy: 0.8571 - val_loss: 1.1152 - val_categorical_accuracy: 0.7280\n",
      "Epoch 11/20\n",
      "1/1 - 0s - loss: 0.7681 - categorical_accuracy: 0.9286 - val_loss: 1.0464 - val_categorical_accuracy: 0.7540\n",
      "Epoch 12/20\n",
      "1/1 - 0s - loss: 0.6665 - categorical_accuracy: 0.9429 - val_loss: 0.9840 - val_categorical_accuracy: 0.7740\n",
      "Epoch 13/20\n",
      "1/1 - 0s - loss: 0.5994 - categorical_accuracy: 0.9500 - val_loss: 0.9309 - val_categorical_accuracy: 0.7820\n",
      "Epoch 14/20\n",
      "1/1 - 0s - loss: 0.5016 - categorical_accuracy: 0.9643 - val_loss: 0.8893 - val_categorical_accuracy: 0.7880\n",
      "Epoch 15/20\n",
      "1/1 - 0s - loss: 0.4481 - categorical_accuracy: 0.9786 - val_loss: 0.8585 - val_categorical_accuracy: 0.7860\n",
      "Epoch 16/20\n",
      "1/1 - 0s - loss: 0.3930 - categorical_accuracy: 0.9786 - val_loss: 0.8370 - val_categorical_accuracy: 0.7840\n",
      "Epoch 17/20\n",
      "1/1 - 0s - loss: 0.3617 - categorical_accuracy: 0.9714 - val_loss: 0.8221 - val_categorical_accuracy: 0.7860\n",
      "Epoch 18/20\n",
      "1/1 - 0s - loss: 0.3515 - categorical_accuracy: 0.9714 - val_loss: 0.8109 - val_categorical_accuracy: 0.7880\n",
      "Epoch 19/20\n",
      "1/1 - 0s - loss: 0.3070 - categorical_accuracy: 0.9857 - val_loss: 0.8035 - val_categorical_accuracy: 0.7880\n",
      "Epoch 20/20\n",
      "1/1 - 0s - loss: 0.2896 - categorical_accuracy: 0.9786 - val_loss: 0.7987 - val_categorical_accuracy: 0.7920\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen, shuffle=False, epochs=20, verbose=2, validation_data=val_gen\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31",
   "metadata": {},
   "source": [
    "Evaluate the trained model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.7585\n",
      "\tcategorical_accuracy: 0.8037\n"
     ]
    }
   ],
   "source": [
    "test_gen = generator.flow(test_subjects.index, test_targets)\n",
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33",
   "metadata": {},
   "source": [
    "## Node and link importance via saliency maps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34",
   "metadata": {},
   "source": [
    "In order to understand why a selected node is predicted as a certain class we want to find the node feature importance, total node importance, and link importance for nodes and edges in the selected node's neighbourhood (ego-net). These importances give information about the effect of changes in the node's features and its neighbourhood on the prediction of the node, specifically:\n",
    "\n",
    "- **Node feature importance**: Given the selected node $t$ and the model's prediction $s(c)$ for class $c$. The feature importance can be calculated for each node $v$ in the selected node's ego-net where the importance of feature $f$ for node $v$ is the change predicted score $s(c)$ for the selected node when the feature $f$ of node $v$ is perturbed.\n",
    "- **Total node importance**: This is defined as the sum of the feature importances for node $v$ for all features. Nodes with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "- **Link importance**: This is defined as the change in the selected node's predicted score $s(c)$ if the link $e=(u, v)$ is removed from the graph. Links with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "\n",
    "Node and link importances can be used to assess the role of nodes and links in model's predictions for the node(s) of interest (the selected node). For datasets like CORA-ML, the features and edges are binary, vanilla gradients may not perform well so we use integrated gradients [[1]](#refs) to compute them.\n",
    "\n",
    "Another interesting application of node and link importances is to identify model vulnerabilities to attacks via perturbing node features and graph structure (see [[2]](#refs))."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35",
   "metadata": {},
   "source": [
    "To investigate these importances we use the StellarGraph `saliency_maps` routines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36",
   "metadata": {},
   "outputs": [],
   "source": [
    "from stellargraph.interpretability.saliency_maps import IntegratedGradients"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37",
   "metadata": {},
   "source": [
    "Select the target node whose prediction is to be interpreted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_nodes = list(G.nodes())\n",
    "target_nid = 1109199\n",
    "target_idx = graph_nodes.index(target_nid)\n",
    "y_true = all_targets[target_idx]  # true class of the target node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected node id: 1109199, \n",
      "True label: [0 1 0 0 0 0 0], \n",
      "Predicted scores: [0.06 0.57 0.15 0.06 0.05 0.04 0.07]\n"
     ]
    }
   ],
   "source": [
    "all_gen = generator.flow(graph_nodes)\n",
    "y_pred = model.predict(all_gen)[0, target_idx]\n",
    "class_of_interest = np.argmax(y_pred)\n",
    "\n",
    "print(\n",
    "    \"Selected node id: {}, \\nTrue label: {}, \\nPredicted scores: {}\".format(\n",
    "        target_nid, y_true, y_pred.round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40",
   "metadata": {},
   "source": [
    "Get the node feature importance by using integrated gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "41",
   "metadata": {},
   "outputs": [],
   "source": [
    "int_grad_saliency = IntegratedGradients(model, train_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42",
   "metadata": {},
   "source": [
    "For the parameters of `get_node_importance` method, `X` and `A` are the feature and adjacency matrices, respectively. `target_idx` is the node of interest, and `class_of_interest` is set as the predicted label of the node. `steps` indicates the number of steps used to approximate the integration in integrated gradients calculation. A larger value of `steps` gives better approximation, at the cost of higher computational overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "integrated_node_importance = int_grad_saliency.get_node_importance(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "integrated_node_importance [0. 0. 0. ... 0. 0. 0.]\n",
      "integrate_node_importance.shape = (2708,)\n",
      "integrated self-importance of target node 1109199: 3.61\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nintegrated_node_importance\", integrated_node_importance.round(2))\n",
    "print(\"integrate_node_importance.shape = {}\".format(integrated_node_importance.shape))\n",
    "print(\n",
    "    \"integrated self-importance of target node {}: {}\".format(\n",
    "        target_nid, integrated_node_importance[target_idx].round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46",
   "metadata": {},
   "source": [
    "Check that number of non-zero node importance values is less or equal the number of nodes in target node's K-hop ego net (where K is the number of GCN layers in the model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "47",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ego = nx.ego_graph(G.to_networkx(), target_nid, radius=len(gcn.activations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the ego graph: 202\n",
      "Number of non-zero elements in integrated_node_importance: 202\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of nodes in the ego graph: {}\".format(len(G_ego.nodes())))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrated_node_importance: {}\".format(\n",
    "        np.count_nonzero(integrated_node_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49",
   "metadata": {},
   "source": [
    "We now compute the link importance using integrated gradients [1]. Integrated gradients are obtained by accumulating the gradients along the path between the baseline (all-zero graph) and the state of the graph. They provide better sensitivity for the graphs with binary features and edges compared with the vanilla gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrate_link_importance.shape = (2708, 2708)\n"
     ]
    }
   ],
   "source": [
    "integrate_link_importance = int_grad_saliency.get_integrated_link_masks(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")\n",
    "print(\"integrate_link_importance.shape = {}\".format(integrate_link_importance.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51",
   "metadata": {},
   "source": [
    "Some sanity checks:\n",
    "We expect the number of non-zero elements in the integrated link importance be same or less than the number of real edges in the ego graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "52",
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, _, A), _ = train_gen[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of edges in the ego graph: 210\n",
      "Number of non-zero elements in integrate_link_importance: 210\n"
     ]
    }
   ],
   "source": [
    "# The built-in number_of_edges function for the ego graph does not count the self-loops and some reversed edges\n",
    "# in the non-directed graph so we do the sanity check as the following.\n",
    "G_ego_edges = set()\n",
    "for i in np.nonzero(A[0, target_idx])[0]:\n",
    "    G_ego_edges.add((graph_nodes[target_idx], graph_nodes[i]))\n",
    "    for j in np.nonzero(A[0, i])[0]:\n",
    "        G_ego_edges.add((graph_nodes[i], graph_nodes[j]))\n",
    "print(\"Number of edges in the ego graph: {}\".format(len(G_ego_edges)))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrate_link_importance: {}\".format(\n",
    "        np.count_nonzero(integrate_link_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54",
   "metadata": {},
   "source": [
    "We can now find the nodes that have the highest importance to the prediction of the selected node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most important links by integrated gradients are:\n",
      " [(6214, 1105764), (6214, 13960), (6214, 345340), (6214, 6378), (6214, 399339), (6214, 124064), (6214, 95589), (6214, 1106172), (148170, 1128997), (1109199, 6214)]\n"
     ]
    }
   ],
   "source": [
    "sorted_indices = np.argsort(integrate_link_importance.flatten())\n",
    "N = len(graph_nodes)\n",
    "integrated_link_importance_rank = [\n",
    "    (graph_nodes[k // N], graph_nodes[k % N]) for k in sorted_indices[::-1]\n",
    "]\n",
    "topk = 10\n",
    "print(\n",
    "    \"Top {} most important links by integrated gradients are:\\n {}\".format(\n",
    "        topk, integrated_link_importance_rank[-topk:]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the labels as an attribute for the nodes in the graph. The labels are used to color the nodes in different classes.\n",
    "nx.set_node_attributes(G_ego, values={x[0]: {\"subject\": x[1]} for x in subjects.items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57",
   "metadata": {},
   "source": [
    "In the following, we plot the link and node importance (computed by integrated gradients) of the nodes within the ego graph of the target node. \n",
    "\n",
    "For nodes, the shape of the node indicates the positive/negative importance the node has. 'round' nodes have positive importance while 'diamond' nodes have negative importance. The size of the node indicates the value of the importance, e.g., a large diamond node has higher negative importance. \n",
    "\n",
    "For links, the color of the link indicates the positive/negative importance the link has. 'red' links have positive importance while 'blue' links have negative importance. The width of the link indicates the value of the importance, e.g., a thicker blue link has higher negative importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.026242917579111"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15404796169424637"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate_link_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_size_factor = 1e2\n",
    "link_width_factor = 2\n",
    "\n",
    "nodes = list(G_ego.nodes())\n",
    "colors = pd.DataFrame(\n",
    "    [v[1][\"subject\"] for v in G_ego.nodes(data=True)], index=nodes, columns=[\"subject\"]\n",
    ")\n",
    "colors = np.argmax(target_encoding.transform(colors), axis=1) + 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "pos = nx.spring_layout(G_ego)\n",
    "\n",
    "# Draw ego as large and red\n",
    "node_sizes = [integrated_node_importance[graph_nodes.index(k)] for k in nodes]\n",
    "node_shapes = [\"o\" if w > 0 else \"d\" for w in node_sizes]\n",
    "\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "node_size_scale = node_size_factor / np.max(node_sizes)\n",
    "for k in range(len(nodes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(nodes[k])\n",
    "        positive_node_sizes.append(node_size_scale * node_sizes[k])\n",
    "\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(nodes[k])\n",
    "        negative_node_sizes.append(node_size_scale * abs(node_sizes[k]))\n",
    "\n",
    "# Plot the ego network with the node importances\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_nid)]),\n",
    "    node_shape=\"*\",\n",
    "    node_color=[colors[nodes.index(target_nid)]],\n",
    "    cmap=cmap,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "\n",
    "# Draw the edges with the edge importances\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    integrate_link_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61",
   "metadata": {},
   "source": [
    "We then remove the node or edge in the ego graph one by one and check how the prediction changes. By doing so, we can obtain the ground truth importance of the nodes and edges. Comparing the following figure and the above one can show the effectiveness of integrated gradients as the importance approximations are relatively consistent with the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_bk = deepcopy(X)\n",
    "A_bk = deepcopy(A)\n",
    "selected_nodes = np.array([[target_idx]], dtype=\"int32\")\n",
    "nodes = [graph_nodes.index(v) for v in G_ego.nodes()]\n",
    "edges = [(graph_nodes.index(u), graph_nodes.index(v)) for u, v in G_ego.edges()]\n",
    "\n",
    "clean_prediction = model.predict([X, selected_nodes, A]).squeeze()\n",
    "predict_label = np.argmax(clean_prediction)\n",
    "\n",
    "groud_truth_node_importance = np.zeros((N,))\n",
    "for node in nodes:\n",
    "    # we set all the features of the node to zero to check the ground truth node importance.\n",
    "    X_perturb = deepcopy(X_bk)\n",
    "    X_perturb[:, node, :] = 0\n",
    "    predict_after_perturb = model.predict([X_perturb, selected_nodes, A]).squeeze()\n",
    "    groud_truth_node_importance[node] = (\n",
    "        clean_prediction[predict_label] - predict_after_perturb[predict_label]\n",
    "    )\n",
    "\n",
    "node_shapes = [\n",
    "    \"o\" if groud_truth_node_importance[k] > 0 else \"d\" for k in range(len(nodes))\n",
    "]\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "# node_size_scale is used for better visulization of nodes\n",
    "node_size_scale = node_size_factor / max(groud_truth_node_importance)\n",
    "\n",
    "for k in range(len(node_shapes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(graph_nodes[nodes[k]])\n",
    "        positive_node_sizes.append(\n",
    "            node_size_scale * groud_truth_node_importance[nodes[k]]\n",
    "        )\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(graph_nodes[nodes[k]])\n",
    "        negative_node_sizes.append(\n",
    "            node_size_scale * abs(groud_truth_node_importance[nodes[k]])\n",
    "        )\n",
    "X = deepcopy(X_bk)\n",
    "groud_truth_edge_importance = np.zeros((N, N))\n",
    "for edge in edges:\n",
    "    A = deepcopy(A_bk)\n",
    "    if A[0, edge[0], edge[1]] == 0:\n",
    "        continue\n",
    "    # we set the weight of a given edge to zero to check the ground truth link importance\n",
    "    A[:, edge[0], edge[1]] = 0\n",
    "    predict_after_perturb = model.predict([X, selected_nodes, A]).squeeze()\n",
    "    groud_truth_edge_importance[edge[0], edge[1]] = (\n",
    "        predict_after_perturb[predict_label] - clean_prediction[predict_label]\n",
    "    ) / (0 - 1)\n",
    "    A[:, edge[0], edge[1]] = 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_idx)]),\n",
    "    node_color=[colors[nodes.index(target_idx)]],\n",
    "    cmap=cmap,\n",
    "    node_shape=\"*\",\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "# Draw the ego net\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    groud_truth_edge_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63",
   "metadata": {},
   "source": [
    "By comparing the above two figures, one can see that the integrated gradients are quite consistent with the brute-force approach. The main benefit of using integrated gradients is scalability. The gradient operations are very efficient to compute on deep learning frameworks with the parallelism provided by GPUs. Also, integrated gradients can give the importance of individual node features, for all nodes in the graph. Achieving this by brute-force approach is often non-trivial. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/interpretability/gcn-node-link-importance.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/interpretability/gcn-node-link-importance.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}