"""TF Module for APPNPConv"""
# pylint: disable= no-member, arguments-differ, invalid-name
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
from .... import function as fn
[docs]class APPNPConv(layers.Layer):
r"""Approximate Personalized Propagation of Neural Predictions
layer from paper `Predict then Propagate: Graph Neural Networks
meet Personalized PageRank <https://arxiv.org/pdf/1810.05997.pdf>`__.
.. math::
H^{0} & = X
H^{t+1} & = (1-\alpha)\left(\hat{D}^{-1/2}
\hat{A} \hat{D}^{-1/2} H^{t}\right) + \alpha H^{0}
Parameters
----------
k : int
Number of iterations :math:`K`.
alpha : float
The teleport probability :math:`\alpha`.
edge_drop : float, optional
Dropout rate on edges that controls the
messages received by each node. Default: ``0``.
"""
def __init__(self,
k,
alpha,
edge_drop=0.):
super(APPNPConv, self).__init__()
self._k = k
self._alpha = alpha
self.edge_drop = layers.Dropout(edge_drop)
def call(self, graph, feat):
r"""Compute APPNP layer.
Parameters
----------
graph : DGLGraph
The graph.
feat : tf.Tensor
The input feature of shape :math:`(N, *)` :math:`N` is the
number of nodes, and :math:`*` could be of any shape.
Returns
-------
tf.Tensor
The output feature of shape :math:`(N, *)` where :math:`*`
should be the same as input shape.
"""
with graph.local_scope():
degs = tf.clip_by_value(tf.cast(graph.in_degrees(), tf.float32),
clip_value_min=1, clip_value_max=np.inf)
norm = tf.pow(degs, -0.5)
shp = norm.shape + (1,) * (feat.ndim - 1)
norm = tf.reshape(norm, shp)
feat_0 = feat
for _ in range(self._k):
# normalization by src node
feat = feat * norm
graph.ndata['h'] = feat
graph.edata['w'] = self.edge_drop(
tf.ones(graph.number_of_edges(), 1))
graph.update_all(fn.u_mul_e('h', 'w', 'm'),
fn.sum('m', 'h'))
feat = graph.ndata.pop('h')
# normalization by dst node
feat = feat * norm
feat = (1 - self._alpha) * feat + self._alpha * feat_0
return feat