Code examples / Graph Data / Graph attention network (GAT) for node classification

Graph attention network (GAT) for node classification

Author: akensert
Date created: 2021/09/13
Last modified: 2021/12/26
Description: An implementation of a Graph Attention Network (GAT) for node classification.

ⓘ This example uses Keras 2

View in Colab GitHub source


Introduction

Graph neural networks is the prefered neural network architecture for processing data structured as graphs (for example, social networks or molecule structures), yielding better results than fully-connected networks or convolutional networks.

In this tutorial, we will implement a specific graph neural network known as a Graph Attention Network (GAT) to predict labels of scientific papers based on what type of papers cite them (using the Cora dataset).

References

For more information on GAT, see the original paper Graph Attention Networks as well as DGL's Graph Attention Networks documentation.

Import packages

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import pandas as pd
import os
import warnings

warnings.filterwarnings("ignore")
pd.set_option("display.max_columns", 6)
pd.set_option("display.max_rows", 6)
np.random.seed(2)

Obtain the dataset

The preparation of the Cora dataset follows that of the Node classification with Graph Neural Networks tutorial. Refer to this tutorial for more details on the dataset and exploratory data analysis. In brief, the Cora dataset consists of two files: cora.cites which contains directed links (citations) between papers; and cora.content which contains features of the corresponding papers and one of seven labels (the subject of the paper).

zip_file = keras.utils.get_file(
    fname="cora.tgz",
    origin="https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz",
    extract=True,
)

data_dir = os.path.join(os.path.dirname(zip_file), "cora")

citations = pd.read_csv(
    os.path.join(data_dir, "cora.cites"),
    sep="\t",
    header=None,
    names=["target", "source"],
)

papers = pd.read_csv(
    os.path.join(data_dir, "cora.content"),
    sep="\t",
    header=None,
    names=["paper_id"] + [f"term_{idx}" for idx in range(1433)] + ["subject"],
)

class_values = sorted(papers["subject"].unique())
class_idx = {name: id for id, name in enumerate(class_values)}
paper_idx = {name: idx for idx, name in enumerate(sorted(papers["paper_id"].unique()))}

papers["paper_id"] = papers["paper_id"].apply(lambda name: paper_idx[name])
citations["source"] = citations["source"].apply(lambda name: paper_idx[name])
citations["target"] = citations["target"].apply(lambda name: paper_idx[name])
papers["subject"] = papers["subject"].apply(lambda value: class_idx[value])

print(citations)

print(papers)
      target  source
0          0      21
1          0     905
2          0     906
...      ...     ...
5426    1874    2586
5427    1876    1874
5428    1897    2707
[5429 rows x 2 columns]
      paper_id  term_0  term_1  ...  term_1431  term_1432  subject
0          462       0       0  ...          0          0        2
1         1911       0       0  ...          0          0        5
2         2002       0       0  ...          0          0        4
...        ...     ...     ...  ...        ...        ...      ...
2705      2372       0       0  ...          0          0        1
2706       955       0       0  ...          0          0        0
2707       376       0       0  ...          0          0        2
[2708 rows x 1435 columns]

Split the dataset

# Obtain random indices
random_indices = np.random.permutation(range(papers.shape[0]))

# 50/50 split
train_data = papers.iloc[random_indices[: len(random_indices) // 2]]
test_data = papers.iloc[random_indices[len(random_indices) // 2 :]]

Prepare the graph data

# Obtain paper indices which will be used to gather node states
# from the graph later on when training the model
train_indices = train_data["paper_id"].to_numpy()
test_indices = test_data["paper_id"].to_numpy()

# Obtain ground truth labels corresponding to each paper_id
train_labels = train_data["subject"].to_numpy()
test_labels = test_data["subject"].to_numpy()

# Define graph, namely an edge tensor and a node feature tensor
edges = tf.convert_to_tensor(citations[["target", "source"]])
node_states = tf.convert_to_tensor(papers.sort_values("paper_id").iloc[:, 1:-1])

# Print shapes of the graph
print("Edges shape:\t\t", edges.shape)
print("Node features shape:", node_states.shape)
Edges shape:         (5429, 2)
Node features shape: (2708, 1433)

Build the model

GAT takes as input a graph (namely an edge tensor and a node feature tensor) and outputs [updated] node states. The node states are, for each target node, neighborhood aggregated information of N-hops (where N is decided by the number of layers of the GAT). Importantly, in contrast to the graph convolutional network (GCN) the GAT makes use of attention machanisms to aggregate information from neighboring nodes (or source nodes). In other words, instead of simply averaging/summing node states from source nodes (source papers) to the target node (target papers), GAT first applies normalized attention scores to each source node state and then sums.

(Multi-head) graph attention layer

The GAT model implements multi-head graph attention layers. The MultiHeadGraphAttention layer is simply a concatenation (or averaging) of multiple graph attention layers (GraphAttention), each with separate learnable weights W. The GraphAttention layer does the following:

Consider inputs node states h^{l} which are linearly transformed by W^{l}, resulting in z^{l}.

For each target node:

  1. Computes pair-wise attention scores a^{l}^{T}(z^{l}_{i}||z^{l}_{j}) for all j, resulting in e_{ij} (for all j). || denotes a concatenation, _{i} corresponds to the target node, and _{j} corresponds to a given 1-hop neighbor/source node.
  2. Normalizes e_{ij} via softmax, so as the sum of incoming edges' attention scores to the target node (sum_{k}{e_{norm}_{ik}}) will add up to 1.
  3. Applies attention scores e_{norm}_{ij} to z_{j} and adds it to the new target node state h^{l+1}_{i}, for all j.
class GraphAttention(layers.Layer):
    def __init__(
        self,
        units,
        kernel_initializer="glorot_uniform",
        kernel_regularizer=None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.units = units
        self.kernel_initializer = keras.initializers.get(kernel_initializer)
        self.kernel_regularizer = keras.regularizers.get(kernel_regularizer)

    def build(self, input_shape):

        self.kernel = self.add_weight(
            shape=(input_shape[0][-1], self.units),
            trainable=True,
            initializer=self.kernel_initializer,
            regularizer=self.kernel_regularizer,
            name="kernel",
        )
        self.kernel_attention = self.add_weight(
            shape=(self.units * 2, 1),
            trainable=True,
            initializer=self.kernel_initializer,
            regularizer=self.kernel_regularizer,
            name="kernel_attention",
        )
        self.built = True

    def call(self, inputs):
        node_states, edges = inputs

        # Linearly transform node states
        node_states_transformed = tf.matmul(node_states, self.kernel)

        # (1) Compute pair-wise attention scores
        node_states_expanded = tf.gather(node_states_transformed, edges)
        node_states_expanded = tf.reshape(
            node_states_expanded, (tf.shape(edges)[0], -1)
        )
        attention_scores = tf.nn.leaky_relu(
            tf.matmul(node_states_expanded, self.kernel_attention)
        )
        attention_scores = tf.squeeze(attention_scores, -1)

        # (2) Normalize attention scores
        attention_scores = tf.math.exp(tf.clip_by_value(attention_scores, -2, 2))
        attention_scores_sum = tf.math.unsorted_segment_sum(
            data=attention_scores,
            segment_ids=edges[:, 0],
            num_segments=tf.reduce_max(edges[:, 0]) + 1,
        )
        attention_scores_sum = tf.repeat(
            attention_scores_sum, tf.math.bincount(tf.cast(edges[:, 0], "int32"))
        )
        attention_scores_norm = attention_scores / attention_scores_sum

        # (3) Gather node states of neighbors, apply attention scores and aggregate
        node_states_neighbors = tf.gather(node_states_transformed, edges[:, 1])
        out = tf.math.unsorted_segment_sum(
            data=node_states_neighbors * attention_scores_norm[:, tf.newaxis],
            segment_ids=edges[:, 0],
            num_segments=tf.shape(node_states)[0],
        )
        return out


class MultiHeadGraphAttention(layers.Layer):
    def __init__(self, units, num_heads=8, merge_type="concat", **kwargs):
        super().__init__(**kwargs)
        self.num_heads = num_heads
        self.merge_type = merge_type
        self.attention_layers = [GraphAttention(units) for _ in range(num_heads)]

    def call(self, inputs):
        atom_features, pair_indices = inputs

        # Obtain outputs from each attention head
        outputs = [
            attention_layer([atom_features, pair_indices])
            for attention_layer in self.attention_layers
        ]
        # Concatenate or average the node states from each head
        if self.merge_type == "concat":
            outputs = tf.concat(outputs, axis=-1)
        else:
            outputs = tf.reduce_mean(tf.stack(outputs, axis=-1), axis=-1)
        # Activate and return node states
        return tf.nn.relu(outputs)

Implement training logic with custom train_step, test_step, and predict_step methods

Notice, the GAT model operates on the entire graph (namely, node_states and edges) in all phases (training, validation and testing). Hence, node_states and edges are passed to the constructor of the keras.Model and used as attributes. The difference between the phases are the indices (and labels), which gathers certain outputs (tf.gather(outputs, indices)).

class GraphAttentionNetwork(keras.Model):
    def __init__(
        self,
        node_states,
        edges,
        hidden_units,
        num_heads,
        num_layers,
        output_dim,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.node_states = node_states
        self.edges = edges
        self.preprocess = layers.Dense(hidden_units * num_heads, activation="relu")
        self.attention_layers = [
            MultiHeadGraphAttention(hidden_units, num_heads) for _ in range(num_layers)
        ]
        self.output_layer = layers.Dense(output_dim)

    def call(self, inputs):
        node_states, edges = inputs
        x = self.preprocess(node_states)
        for attention_layer in self.attention_layers:
            x = attention_layer([x, edges]) + x
        outputs = self.output_layer(x)
        return outputs

    def train_step(self, data):
        indices, labels = data

        with tf.GradientTape() as tape:
            # Forward pass
            outputs = self([self.node_states, self.edges])
            # Compute loss
            loss = self.compiled_loss(labels, tf.gather(outputs, indices))
        # Compute gradients
        grads = tape.gradient(loss, self.trainable_weights)
        # Apply gradients (update weights)
        optimizer.apply_gradients(zip(grads, self.trainable_weights))
        # Update metric(s)
        self.compiled_metrics.update_state(labels, tf.gather(outputs, indices))

        return {m.name: m.result() for m in self.metrics}

    def predict_step(self, data):
        indices = data
        # Forward pass
        outputs = self([self.node_states, self.edges])
        # Compute probabilities
        return tf.nn.softmax(tf.gather(outputs, indices))

    def test_step(self, data):
        indices, labels = data
        # Forward pass
        outputs = self([self.node_states, self.edges])
        # Compute loss
        loss = self.compiled_loss(labels, tf.gather(outputs, indices))
        # Update metric(s)
        self.compiled_metrics.update_state(labels, tf.gather(outputs, indices))

        return {m.name: m.result() for m in self.metrics}

Train and evaluate

# Define hyper-parameters
HIDDEN_UNITS = 100
NUM_HEADS = 8
NUM_LAYERS = 3
OUTPUT_DIM = len(class_values)

NUM_EPOCHS = 100
BATCH_SIZE = 256
VALIDATION_SPLIT = 0.1
LEARNING_RATE = 3e-1
MOMENTUM = 0.9

loss_fn = keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = keras.optimizers.SGD(LEARNING_RATE, momentum=MOMENTUM)
accuracy_fn = keras.metrics.SparseCategoricalAccuracy(name="acc")
early_stopping = keras.callbacks.EarlyStopping(
    monitor="val_acc", min_delta=1e-5, patience=5, restore_best_weights=True
)

# Build model
gat_model = GraphAttentionNetwork(
    node_states, edges, HIDDEN_UNITS, NUM_HEADS, NUM_LAYERS, OUTPUT_DIM
)

# Compile model
gat_model.compile(loss=loss_fn, optimizer=optimizer, metrics=[accuracy_fn])

gat_model.fit(
    x=train_indices,
    y=train_labels,
    validation_split=VALIDATION_SPLIT,
    batch_size=BATCH_SIZE,
    epochs=NUM_EPOCHS,
    callbacks=[early_stopping],
    verbose=2,
)

_, test_accuracy = gat_model.evaluate(x=test_indices, y=test_labels, verbose=0)

print("--" * 38 + f"\nTest Accuracy {test_accuracy*100:.1f}%")
Epoch 1/100
5/5 - 26s - loss: 1.8418 - acc: 0.2980 - val_loss: 1.5117 - val_acc: 0.4044 - 26s/epoch - 5s/step
Epoch 2/100
5/5 - 6s - loss: 1.2422 - acc: 0.5640 - val_loss: 1.0407 - val_acc: 0.6471 - 6s/epoch - 1s/step
Epoch 3/100
5/5 - 5s - loss: 0.7092 - acc: 0.7906 - val_loss: 0.8201 - val_acc: 0.7868 - 5s/epoch - 996ms/step
Epoch 4/100
5/5 - 5s - loss: 0.4768 - acc: 0.8604 - val_loss: 0.7451 - val_acc: 0.8088 - 5s/epoch - 934ms/step
Epoch 5/100
5/5 - 5s - loss: 0.2641 - acc: 0.9294 - val_loss: 0.7499 - val_acc: 0.8088 - 5s/epoch - 945ms/step
Epoch 6/100
5/5 - 5s - loss: 0.1487 - acc: 0.9663 - val_loss: 0.6803 - val_acc: 0.8382 - 5s/epoch - 967ms/step
Epoch 7/100
5/5 - 5s - loss: 0.0970 - acc: 0.9811 - val_loss: 0.6688 - val_acc: 0.8088 - 5s/epoch - 960ms/step
Epoch 8/100
5/5 - 5s - loss: 0.0597 - acc: 0.9934 - val_loss: 0.7295 - val_acc: 0.8162 - 5s/epoch - 981ms/step
Epoch 9/100
5/5 - 5s - loss: 0.0398 - acc: 0.9967 - val_loss: 0.7551 - val_acc: 0.8309 - 5s/epoch - 991ms/step
Epoch 10/100
5/5 - 5s - loss: 0.0312 - acc: 0.9984 - val_loss: 0.7666 - val_acc: 0.8309 - 5s/epoch - 987ms/step
Epoch 11/100
5/5 - 5s - loss: 0.0219 - acc: 0.9992 - val_loss: 0.7726 - val_acc: 0.8309 - 5s/epoch - 1s/step
----------------------------------------------------------------------------
Test Accuracy 76.5%

Predict (probabilities)

test_probs = gat_model.predict(x=test_indices)

mapping = {v: k for (k, v) in class_idx.items()}

for i, (probs, label) in enumerate(zip(test_probs[:10], test_labels[:10])):
    print(f"Example {i+1}: {mapping[label]}")
    for j, c in zip(probs, class_idx.keys()):
        print(f"\tProbability of {c: <24} = {j*100:7.3f}%")
    print("---" * 20)
Example 1: Probabilistic_Methods
    Probability of Case_Based               =   0.919%
    Probability of Genetic_Algorithms       =   0.180%
    Probability of Neural_Networks          =  37.896%
    Probability of Probabilistic_Methods    =  59.801%
    Probability of Reinforcement_Learning   =   0.705%
    Probability of Rule_Learning            =   0.044%
    Probability of Theory                   =   0.454%
------------------------------------------------------------
Example 2: Genetic_Algorithms
    Probability of Case_Based               =   0.005%
    Probability of Genetic_Algorithms       =  99.993%
    Probability of Neural_Networks          =   0.001%
    Probability of Probabilistic_Methods    =   0.000%
    Probability of Reinforcement_Learning   =   0.000%
    Probability of Rule_Learning            =   0.000%
    Probability of Theory                   =   0.000%
------------------------------------------------------------
Example 3: Theory
    Probability of Case_Based               =   8.151%
    Probability of Genetic_Algorithms       =   1.021%
    Probability of Neural_Networks          =   0.569%
    Probability of Probabilistic_Methods    =  40.220%
    Probability of Reinforcement_Learning   =   0.792%
    Probability of Rule_Learning            =   6.910%
    Probability of Theory                   =  42.337%
------------------------------------------------------------
Example 4: Neural_Networks
    Probability of Case_Based               =   0.097%
    Probability of Genetic_Algorithms       =   0.026%
    Probability of Neural_Networks          =  93.539%
    Probability of Probabilistic_Methods    =   6.206%
    Probability of Reinforcement_Learning   =   0.028%
    Probability of Rule_Learning            =   0.010%
    Probability of Theory                   =   0.094%
------------------------------------------------------------
Example 5: Theory
    Probability of Case_Based               =  25.259%
    Probability of Genetic_Algorithms       =   4.381%
    Probability of Neural_Networks          =  11.776%
    Probability of Probabilistic_Methods    =  15.053%
    Probability of Reinforcement_Learning   =   1.571%
    Probability of Rule_Learning            =  23.589%
    Probability of Theory                   =  18.370%
------------------------------------------------------------
Example 6: Genetic_Algorithms
    Probability of Case_Based               =   0.000%
    Probability of Genetic_Algorithms       = 100.000%
    Probability of Neural_Networks          =   0.000%
    Probability of Probabilistic_Methods    =   0.000%
    Probability of Reinforcement_Learning   =   0.000%
    Probability of Rule_Learning            =   0.000%
    Probability of Theory                   =   0.000%
------------------------------------------------------------
Example 7: Neural_Networks
    Probability of Case_Based               =   0.296%
    Probability of Genetic_Algorithms       =   0.291%
    Probability of Neural_Networks          =  93.419%
    Probability of Probabilistic_Methods    =   5.696%
    Probability of Reinforcement_Learning   =   0.050%
    Probability of Rule_Learning            =   0.072%
    Probability of Theory                   =   0.177%
------------------------------------------------------------
Example 8: Genetic_Algorithms
    Probability of Case_Based               =   0.000%
    Probability of Genetic_Algorithms       = 100.000%
    Probability of Neural_Networks          =   0.000%
    Probability of Probabilistic_Methods    =   0.000%
    Probability of Reinforcement_Learning   =   0.000%
    Probability of Rule_Learning            =   0.000%
    Probability of Theory                   =   0.000%
------------------------------------------------------------
Example 9: Theory
    Probability of Case_Based               =   4.103%
    Probability of Genetic_Algorithms       =   5.217%
    Probability of Neural_Networks          =  14.532%
    Probability of Probabilistic_Methods    =  66.747%
    Probability of Reinforcement_Learning   =   3.008%
    Probability of Rule_Learning            =   1.782%
    Probability of Theory                   =   4.611%
------------------------------------------------------------
Example 10: Case_Based
    Probability of Case_Based               =  99.566%
    Probability of Genetic_Algorithms       =   0.017%
    Probability of Neural_Networks          =   0.016%
    Probability of Probabilistic_Methods    =   0.155%
    Probability of Reinforcement_Learning   =   0.026%
    Probability of Rule_Learning            =   0.192%
    Probability of Theory                   =   0.028%
------------------------------------------------------------

Conclusions

The results look OK! The GAT model seems to correctly predict the subjects of the papers, based on what they cite, about 80% of the time. Further improvements could be made by fine-tuning the hyper-parameters of the GAT. For instance, try changing the number of layers, the number of hidden units, or the optimizer/learning rate; add regularization (e.g., dropout); or modify the preprocessing step. We could also try to implement self-loops (i.e., paper X cites paper X) and/or make the graph undirected.