SAGEConv

class dgl.nn.tensorflow.conv.SAGEConv(*args, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

GraphSAGE layer from Inductive Representation Learning on Large Graphs

\[ \begin{align}\begin{aligned}h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate} \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)\\h_{i}^{(l+1)} &= \sigma \left(W \cdot \mathrm{concat} (h_{i}^{l}, h_{\mathcal{N}(i)}^{l+1}) \right)\\h_{i}^{(l+1)} &= \mathrm{norm}(h_{i}^{(l+1)})\end{aligned}\end{align} \]

Parameters

• in_feats (int, or pair of ints) –

  Input feature size; i.e, the number of dimensions of \(h_i^{(l)}\).

  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 \(h_i^{(l+1)}\).

• 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
<tf.Tensor: shape=(6, 2), dtype=float32, numpy=
array([[-3.6633523 , -0.90711546],
    [-3.6633523 , -0.90711546],
    [-3.6633523 , -0.90711546],
    [-3.6633523 , -0.90711546],
    [-3.6633523 , -0.90711546],
    [-3.6633523 , -0.90711546]], dtype=float32)>

>>> # 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
<tf.Tensor: shape=(4, 2), dtype=float32, numpy=
array([[-0.59453356, -0.4055441 ],
    [-0.47459763, -0.717764  ],
    [ 0.3221837 , -0.29876417],
    [-0.63356155,  0.09390211]], dtype=float32)>