chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
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+"""TF Module for APPNPConv"""
+# pylint: disable= no-member, arguments-differ, invalid-name
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras import layers
+
+from .... import function as fn
+
+
+class APPNPConv(layers.Layer):
+    r"""Approximate Personalized Propagation of Neural Predictions
+    layer from `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=0.0):
+        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.num_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