Source code for dgl.nn.tensorflow.conv.sgconv

"""tf Module for Simplifying Graph Convolution layer"""
# pylint: disable= no-member, arguments-differ, invalid-name, W0613
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np

from .... import function as fn

[docs]class SGConv(layers.Layer): r"""Simplifying Graph Convolution layer from paper `Simplifying Graph Convolutional Networks <>`__. .. math:: H^{l+1} = (\hat{D}^{-1/2} \hat{A} \hat{D}^{-1/2})^K H^{l} \Theta^{l} Parameters ---------- in_feats : int Number of input features. out_feats : int Number of output features. k : int Number of hops :math:`K`. Defaults:``1``. cached : bool If True, the module would cache .. math:: (\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}})^K X\Theta at the first forward call. This parameter should only be set to ``True`` in Transductive Learning setting. bias : bool If True, adds a learnable bias to the output. Default: ``True``. norm : callable activation function/layer or None, optional If not None, applies normalization to the updated node features. """ def __init__(self, in_feats, out_feats, k=1, cached=False, bias=True, norm=None): super(SGConv, self).__init__() self.fc = layers.Dense(out_feats, use_bias=bias) self._cached = cached self._cached_h = None self._k = k self.norm = norm def call(self, graph, feat): r"""Compute Simplifying Graph Convolution layer. Parameters ---------- graph : DGLGraph The graph. feat : tf.Tensor The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}` is size of input feature, :math:`N` is the number of nodes. Returns ------- tf.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size of output feature. Notes ----- If ``cache`` is se to True, ``feat`` and ``graph`` should not change during training, or you will get wrong results. """ graph = graph.local_var() if self._cached_h is not None: feat = self._cached_h else: # compute normalization 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) norm = tf.expand_dims(norm, 1) # compute (D^-1 A^k D)^k X for _ in range(self._k): feat = feat * norm graph.ndata['h'] = feat graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h')) feat = graph.ndata.pop('h') feat = feat * norm if self.norm is not None: feat = self.norm(feat) # cache feature if self._cached: self._cached_h = feat return self.fc(feat)