"""Tensorflow Module for GraphSAGE layer""" # pylint: disable= no-member, arguments-differ, invalid-name import tensorflow as tf from tensorflow.keras import layers from .... import function as fn from ....base import DGLError from ....utils import check_eq_shape, expand_as_pair class SAGEConv(layers.Layer): r"""GraphSAGE layer from `Inductive Representation Learning on Large Graphs `__ .. math:: h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate} \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right) h_{i}^{(l+1)} &= \sigma \left(W \cdot \mathrm{concat} (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right) h_{i}^{(l+1)} &= \mathrm{norm}(h_{i}^{(l+1)}) Parameters ---------- in_feats : int, or pair of ints Input feature size; i.e, the number of dimensions of :math:`h_i^{(l)}`. GATConv can be applied on homogeneous graph and unidirectional `bipartite graph `__. If the layer applies on a unidirectional bipartite graph, ``in_feats`` specifies the input feature size on both the source and destination nodes. If a scalar is given, the source and destination node feature size would take the same value. If aggregator type is ``gcn``, the feature size of source and destination nodes are required to be the same. out_feats : int Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`. aggregator_type : str Aggregator type to use (``mean``, ``gcn``, ``pool``, ``lstm``). feat_drop : float Dropout rate on features, default: ``0``. 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. activation : callable activation function/layer or None, optional If not None, applies an activation function to the updated node features. Default: ``None``. Examples -------- >>> import dgl >>> import numpy as np >>> import tensorflow as tf >>> from dgl.nn import SAGEConv >>> >>> # Case 1: Homogeneous graph >>> with tf.device("CPU:0"): >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])) >>> g = dgl.add_self_loop(g) >>> feat = tf.ones((6, 10)) >>> conv = SAGEConv(10, 2, 'pool') >>> res = conv(g, feat) >>> res >>> # Case 2: Unidirectional bipartite graph >>> with tf.device("CPU:0"): >>> u = [0, 1, 0, 0, 1] >>> v = [0, 1, 2, 3, 2] >>> g = dgl.heterograph({('_N', '_E', '_N'):(u, v)}) >>> u_fea = tf.convert_to_tensor(np.random.rand(2, 5)) >>> v_fea = tf.convert_to_tensor(np.random.rand(4, 5)) >>> conv = SAGEConv((5, 10), 2, 'mean') >>> res = conv(g, (u_fea, v_fea)) >>> res """ def __init__( self, in_feats, out_feats, aggregator_type, feat_drop=0.0, bias=True, norm=None, activation=None, ): super(SAGEConv, self).__init__() valid_aggre_types = {"mean", "gcn", "pool", "lstm"} if aggregator_type not in valid_aggre_types: raise DGLError( "Invalid aggregator_type. Must be one of {}. " "But got {!r} instead.".format( valid_aggre_types, aggregator_type ) ) self._in_src_feats, self._in_dst_feats = expand_as_pair(in_feats) self._out_feats = out_feats self._aggre_type = aggregator_type self.norm = norm self.feat_drop = layers.Dropout(feat_drop) self.activation = activation # aggregator type: mean/pool/lstm/gcn if aggregator_type == "pool": self.fc_pool = layers.Dense(self._in_src_feats) if aggregator_type == "lstm": self.lstm = layers.LSTM(units=self._in_src_feats) if aggregator_type != "gcn": self.fc_self = layers.Dense(out_feats, use_bias=bias) self.fc_neigh = layers.Dense(out_feats, use_bias=bias) def _lstm_reducer(self, nodes): """LSTM reducer NOTE(zihao): lstm reducer with default schedule (degree bucketing) is slow, we could accelerate this with degree padding in the future. """ m = nodes.mailbox["m"] # (B, L, D) rst = self.lstm(m) return {"neigh": rst} def call(self, graph, feat): r"""Compute GraphSAGE layer. Parameters ---------- graph : DGLGraph The graph. feat : tf.Tensor or pair of tf.Tensor If a tf.Tensor is given, it represents 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. If a pair of tf.Tensor is given, the pair must contain two tensors of shape :math:`(N_{in}, D_{in_{src}})` and :math:`(N_{out}, D_{in_{dst}})`. Returns ------- tf.Tensor The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}` is size of output feature. """ with graph.local_scope(): if isinstance(feat, tuple): feat_src = self.feat_drop(feat[0]) feat_dst = self.feat_drop(feat[1]) else: feat_src = feat_dst = self.feat_drop(feat) if graph.is_block: feat_dst = feat_src[: graph.number_of_dst_nodes()] h_self = feat_dst # Handle the case of graphs without edges if graph.num_edges() == 0: graph.dstdata["neigh"] = tf.cast( tf.zeros((graph.number_of_dst_nodes(), self._in_src_feats)), tf.float32, ) if self._aggre_type == "mean": graph.srcdata["h"] = feat_src graph.update_all(fn.copy_u("h", "m"), fn.mean("m", "neigh")) h_neigh = graph.dstdata["neigh"] elif self._aggre_type == "gcn": check_eq_shape(feat) graph.srcdata["h"] = feat_src graph.dstdata["h"] = feat_dst # same as above if homogeneous graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "neigh")) # divide in_degrees degs = tf.cast(graph.in_degrees(), tf.float32) h_neigh = (graph.dstdata["neigh"] + graph.dstdata["h"]) / ( tf.expand_dims(degs, -1) + 1 ) elif self._aggre_type == "pool": graph.srcdata["h"] = tf.nn.relu(self.fc_pool(feat_src)) graph.update_all(fn.copy_u("h", "m"), fn.max("m", "neigh")) h_neigh = graph.dstdata["neigh"] elif self._aggre_type == "lstm": graph.srcdata["h"] = feat_src graph.update_all(fn.copy_u("h", "m"), self._lstm_reducer) h_neigh = graph.dstdata["neigh"] else: raise KeyError( "Aggregator type {} not recognized.".format( self._aggre_type ) ) # GraphSAGE GCN does not require fc_self. if self._aggre_type == "gcn": rst = self.fc_neigh(h_neigh) else: rst = self.fc_self(h_self) + self.fc_neigh(h_neigh) # activation if self.activation is not None: rst = self.activation(rst) # normalization if self.norm is not None: rst = self.norm(rst) return rst