from tensorflow.keras.layers import Dense, Lambda, Layer, Dropout, Input
import tensorflow.keras.backend as K
import tensorflow as tf
import numpy as np
from .misc import SqueezedSparseConversion
from ..mapper import FullBatchNodeGenerator
from .preprocessing_layer import GraphPreProcessingLayer
[docs]class PPNPPropagationLayer(Layer):
"""
Implementation of Personalized Propagation of Neural Predictions (PPNP)
as in https://arxiv.org/abs/1810.05997.
Notes:
- The inputs are tensors with a batch dimension of 1:
Keras requires this batch dimension, and for full-batch methods
we only have a single "batch".
- There are three inputs required, the node features, the output
indices (the nodes that are to be selected in the final layer)
and the graph personalized page rank matrix
- This class assumes that the personalized page rank matrix (specified in paper) matrix is passed as
input to the Keras methods.
- The output indices are used when ``final_layer=True`` and the returned outputs
are the final-layer features for the nodes indexed by output indices.
- If ``final_layer=False`` all the node features are output in the same ordering as
given by the adjacency matrix.
Args:
units (int): dimensionality of output feature vectors
final_layer (bool): If False the layer returns output for all nodes,
if True it returns the subset specified by the indices passed to it.
"""
def __init__(self, units, final_layer=False, **kwargs):
if "input_shape" not in kwargs and "input_dim" in kwargs:
kwargs["input_shape"] = (kwargs.get("input_dim"),)
super().__init__(**kwargs)
self.units = units
self.final_layer = final_layer
[docs] def get_config(self):
"""
Gets class configuration for Keras serialization.
Used by keras model serialization.
Returns:
A dictionary that contains the config of the layer
"""
config = {"units": self.units, "final_layer": self.final_layer}
base_config = super().get_config()
return {**base_config, **config}
[docs] def compute_output_shape(self, input_shapes):
"""
Computes the output shape of the layer.
Assumes the following inputs:
Args:
input_shapes (tuple of ints)
Shape tuples can include None for free dimensions, instead of an integer.
Returns:
An input shape tuple.
"""
feature_shape, out_shape, *As_shapes = input_shapes
batch_dim = feature_shape[0]
if self.final_layer:
out_dim = out_shape[1]
else:
out_dim = feature_shape[1]
return batch_dim, out_dim, self.units
[docs] def build(self, input_shapes):
"""
Builds the layer
Args:
input_shapes (list of int): shapes of the layer's inputs (node features and adjacency matrix)
"""
self.built = True
[docs] def call(self, inputs):
"""
Applies the layer.
Args:
inputs (list): a list of 3 input tensors that includes
node features (size 1 x N x F),
output indices (size 1 x M)
graph personalized page rank matrix (size N x N),
where N is the number of nodes in the graph, and
F is the dimensionality of node features.
Returns:
Keras Tensor that represents the output of the layer.
"""
features, out_indices, *As = inputs
batch_dim, n_nodes, _ = K.int_shape(features)
if batch_dim != 1:
raise ValueError(
"Currently full-batch methods only support a batch dimension of one"
)
# Remove singleton batch dimension
features = K.squeeze(features, 0)
out_indices = K.squeeze(out_indices, 0)
# Propagate the features
A = As[0]
output = K.dot(A, features)
# On the final layer we gather the nodes referenced by the indices
if self.final_layer:
output = K.gather(output, out_indices)
# Add batch dimension back if we removed it
if batch_dim == 1:
output = K.expand_dims(output, 0)
return output
[docs]class PPNP:
"""
Implementation of Personalized Propagation of Neural Predictions (PPNP)
as in https://arxiv.org/abs/1810.05997.
The model minimally requires specification of the fully connected layer sizes as a list of ints
corresponding to the feature dimensions for each hidden layer,
activation functions for each hidden layers, and a generator object.
To use this class as a Keras model, the features and pre-processed adjacency matrix
should be supplied using the :class:`FullBatchNodeGenerator` class. To have the appropriate
pre-processing the generator object should be instantiated as follows::
generator = FullBatchNodeGenerator(G, method="ppnp")
Notes:
- The inputs are tensors with a batch dimension of 1. These are provided by the \
:class:`FullBatchNodeGenerator` object.
- This assumes that the personalized page rank matrix is provided as input to
Keras methods. When using the :class:`FullBatchNodeGenerator` specify the
``method='ppnp'`` argument to do this pre-processing.
- ''method='ppnp'`` requires that use_spare=False and generates a dense personalized page rank matrix matrix
- The nodes provided to the :class:`FullBatchNodeGenerator.flow` method are
used by the final layer to select the predictions for those nodes in order.
However, the intermediate layers before the final layer order the nodes
in the same way as the adjacency matrix.
- The size of the final fully connected layer must be equal to the number of classes of classes to predict.
Args:
layer_sizes (list of int): list of output sizes of fully connected layers in the stack
activations (list of str): list of activations applied to each fully connected layer's output
generator (FullBatchNodeGenerator): an instance of FullBatchNodeGenerator class constructed on the graph of interest
bias (bool): toggles an optional bias in fully connected layers
dropout (float): dropout rate applied to input features of each layer
kernel_regularizer (str): normalization applied to the kernels of fully connetcted layers
"""
def __init__(
self,
layer_sizes,
activations,
generator,
bias=True,
dropout=0.0,
kernel_regularizer=None,
):
if not isinstance(generator, FullBatchNodeGenerator):
raise TypeError("Generator should be a instance of FullBatchNodeGenerator")
if not len(layer_sizes) == len(activations):
raise ValueError(
"The number of layers should equal the number of activations"
)
self.layer_sizes = layer_sizes
self.activations = activations
self.bias = bias
self.dropout = dropout
self.kernel_regularizer = kernel_regularizer
self.generator = generator
self.support = 1
self.method = generator.method
# Check if the generator is producing a sparse matrix
self.use_sparse = generator.use_sparse
# Initialize a stack of fully connected layers
n_layers = len(self.layer_sizes)
self._layers = []
for ii in range(n_layers):
l = self.layer_sizes[ii]
a = self.activations[ii]
self._layers.append(Dropout(self.dropout))
self._layers.append(
Dense(
l,
activation=a,
use_bias=self.bias,
kernel_regularizer=self.kernel_regularizer,
)
)
self._layers.append(Dropout(self.dropout))
self._layers.append(
PPNPPropagationLayer(self.layer_sizes[-1], final_layer=True)
)
def __call__(self, x):
"""
Apply PPNP to the inputs.
The input tensors are expected to be a list of the following:
[
Node features shape (1, N, F),
Adjacency indices (1, E, 2),
Adjacency values (1, E),
Output indices (1, O)
]
where N is the number of nodes, F the number of input features,
E is the number of edges, O the number of output nodes.
Args:
x (Tensor): input tensors
Returns:
Output tensor
"""
x_in, out_indices, *As = x
# Currently we require the batch dimension to be one for full-batch methods
batch_dim, n_nodes, _ = K.int_shape(x_in)
if batch_dim != 1:
raise ValueError(
"Currently full-batch methods only support a batch dimension of one"
)
# Convert input indices & values to a sparse matrix
if self.use_sparse:
A_indices, A_values = As
Ainput = [
SqueezedSparseConversion(
shape=(n_nodes, n_nodes), dtype=A_values.dtype
)([A_indices, A_values])
]
# Otherwise, create dense matrix from input tensor
else:
Ainput = [Lambda(lambda A: K.squeeze(A, 0))(A) for A in As]
# TODO: Support multiple matrices?
if len(Ainput) != 1:
raise NotImplementedError(
"The APPNP method currently only accepts a single matrix"
)
h_layer = x_in
for layer in self._layers:
if isinstance(layer, PPNPPropagationLayer):
h_layer = layer([h_layer, out_indices] + Ainput)
else:
h_layer = layer(h_layer)
return h_layer
[docs] def node_model(self):
"""
Builds a PPNP model for node prediction
Returns:
tuple: `(x_inp, x_out)`, where `x_inp` is a list of two Keras input tensors for the PPNP model (containing node features and graph adjacency),
and `x_out` is a Keras tensor for the PPNP model output.
"""
# Placeholder for node features
N_nodes = self.generator.features.shape[0]
N_feat = self.generator.features.shape[1]
# Inputs for features & target indices
x_t = Input(batch_shape=(1, N_nodes, N_feat))
out_indices_t = Input(batch_shape=(1, None), dtype="int32")
# Create inputs for sparse or dense matrices
if self.use_sparse:
# Placeholders for the sparse adjacency matrix
A_indices_t = Input(batch_shape=(1, None, 2), dtype="int64")
A_values_t = Input(batch_shape=(1, None))
A_placeholders = [A_indices_t, A_values_t]
else:
# Placeholders for the dense adjacency matrix
A_m = Input(batch_shape=(1, N_nodes, N_nodes))
A_placeholders = [A_m]
# TODO: Support multiple matrices
x_inp = [x_t, out_indices_t] + A_placeholders
x_out = self(x_inp)
# Flatten output by removing singleton batch dimension
if x_out.shape[0] == 1:
self.x_out_flat = Lambda(lambda x: K.squeeze(x, 0))(x_out)
else:
self.x_out_flat = x_out
return x_inp, x_out