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

"""Tensorflow Module for Relational graph convolution layer"""
# pylint: disable= no-member, arguments-differ, invalid-name
import tensorflow as tf
from tensorflow.keras import layers

from .... import function as fn
from .. import utils

[docs]class RelGraphConv(layers.Layer): r"""Relational graph convolution layer. Relational graph convolution is introduced in "`Modeling Relational Data with Graph Convolutional Networks <>`__" and can be described as below: .. math:: h_i^{(l+1)} = \sigma(\sum_{r\in\mathcal{R}} \sum_{j\in\mathcal{N}^r(i)}\frac{1}{c_{i,r}}W_r^{(l)}h_j^{(l)}+W_0^{(l)}h_i^{(l)}) where :math:`\mathcal{N}^r(i)` is the neighbor set of node :math:`i` w.r.t. relation :math:`r`. :math:`c_{i,r}` is the normalizer equal to :math:`|\mathcal{N}^r(i)|`. :math:`\sigma` is an activation function. :math:`W_0` is the self-loop weight. The basis regularization decomposes :math:`W_r` by: .. math:: W_r^{(l)} = \sum_{b=1}^B a_{rb}^{(l)}V_b^{(l)} where :math:`B` is the number of bases. The block-diagonal-decomposition regularization decomposes :math:`W_r` into :math:`B` number of block diagonal matrices. We refer :math:`B` as the number of bases. Parameters ---------- in_feat : int Input feature size. out_feat : int Output feature size. num_rels : int Number of relations. regularizer : str Which weight regularizer to use "basis" or "bdd" num_bases : int, optional Number of bases. If is none, use number of relations. Default: None. bias : bool, optional True if bias is added. Default: True activation : callable, optional Activation function. Default: None self_loop : bool, optional True to include self loop message. Default: False dropout : float, optional Dropout rate. Default: 0.0 """ def __init__(self, in_feat, out_feat, num_rels, regularizer="basis", num_bases=None, bias=True, activation=None, self_loop=False, dropout=0.0): super(RelGraphConv, self).__init__() self.in_feat = in_feat self.out_feat = out_feat self.num_rels = num_rels self.regularizer = regularizer self.num_bases = num_bases if self.num_bases is None or self.num_bases > self.num_rels or self.num_bases < 0: self.num_bases = self.num_rels self.bias = bias self.activation = activation self.self_loop = self_loop xinit = tf.keras.initializers.glorot_uniform() zeroinit = tf.keras.initializers.zeros() if regularizer == "basis": # add basis weights self.weight = tf.Variable(initial_value=xinit( shape=(self.num_bases, self.in_feat, self.out_feat), dtype='float32'), trainable=True) if self.num_bases < self.num_rels: # linear combination coefficients self.w_comp = tf.Variable(initial_value=xinit( shape=(self.num_rels, self.num_bases), dtype='float32'), trainable=True) # message func self.message_func = self.basis_message_func elif regularizer == "bdd": if in_feat % num_bases != 0 or out_feat % num_bases != 0: raise ValueError( 'Feature size must be a multiplier of num_bases.') # add block diagonal weights self.submat_in = in_feat // self.num_bases self.submat_out = out_feat // self.num_bases # assuming in_feat and out_feat are both divisible by num_bases self.weight = tf.Variable(initial_value=xinit( shape=(self.num_rels, self.num_bases * self.submat_in * self.submat_out), dtype='float32'), trainable=True) # message func self.message_func = self.bdd_message_func else: raise ValueError("Regularizer must be either 'basis' or 'bdd'") # bias if self.bias: self.h_bias = tf.Variable(initial_value=zeroinit( shape=(out_feat), dtype='float32'), trainable=True) # weight for self loop if self.self_loop: self.loop_weight = tf.Variable(initial_value=xinit( shape=(in_feat, out_feat), dtype='float32'), trainable=True) self.dropout = layers.Dropout(rate=dropout) def basis_message_func(self, edges): """Message function for basis regularizer""" if self.num_bases < self.num_rels: # generate all weights from bases weight = tf.reshape(self.weight, (self.num_bases, self.in_feat * self.out_feat)) weight = tf.reshape(tf.matmul(self.w_comp, weight), ( self.num_rels, self.in_feat, self.out_feat)) else: weight = self.weight msg = utils.bmm_maybe_select( edges.src['h'], weight,['type']) if 'norm' in msg = msg *['norm'] return {'msg': msg} def bdd_message_func(self, edges): """Message function for block-diagonal-decomposition regularizer""" if ((edges.src['h'].dtype == tf.int64) and len(edges.src['h'].shape) == 1): raise TypeError( 'Block decomposition does not allow integer ID feature.') weight = tf.reshape(tf.gather( self.weight,['type']), (-1, self.submat_in, self.submat_out)) node = tf.reshape(edges.src['h'], (-1, 1, self.submat_in)) msg = tf.reshape(tf.matmul(node, weight), (-1, self.out_feat)) if 'norm' in msg = msg *['norm'] return {'msg': msg} def call(self, g, x, etypes, norm=None): """ Forward computation Parameters ---------- g : DGLGraph The graph. x : tf.Tensor Input node features. Could be either * :math:`(|V|, D)` dense tensor * :math:`(|V|,)` int64 vector, representing the categorical values of each node. We then treat the input feature as an one-hot encoding feature. etypes : tf.Tensor Edge type tensor. Shape: :math:`(|E|,)` norm : tf.Tensor Optional edge normalizer tensor. Shape: :math:`(|E|, 1)` Returns ------- tf.Tensor New node features. """ assert g.is_homograph(), \ "not a homograph; convert it with to_homo and pass in the edge type as argument" with g.local_scope(): g.ndata['h'] = x g.edata['type'] = tf.cast(etypes, tf.int64) if norm is not None: g.edata['norm'] = norm if self.self_loop: loop_message = utils.matmul_maybe_select(x, self.loop_weight) # message passing g.update_all(self.message_func, fn.sum(msg='msg', out='h')) # apply bias and activation node_repr = g.ndata['h'] if self.bias: node_repr = node_repr + self.h_bias if self.self_loop: node_repr = node_repr + loop_message if self.activation: node_repr = self.activation(node_repr) node_repr = self.dropout(node_repr) return node_repr