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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+"""MXNet Module for APPNPConv"""
+# pylint: disable= no-member, arguments-differ, invalid-name
+import mxnet as mx
+from mxnet import nd
+from mxnet.gluon import nn
+
+from .... import function as fn
+
+
+class APPNPConv(nn.Block):
+    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^{l+1} &= (1-\alpha)\left(\tilde{D}^{-1/2}
+        \tilde{A} \tilde{D}^{-1/2} H^{l}\right) + \alpha H^{0}
+
+    where :math:`\tilde{A}` is :math:`A` + :math:`I`.
+
+    Parameters
+    ----------
+    k : int
+        The number of iterations :math:`K`.
+    alpha : float
+        The teleport probability :math:`\alpha`.
+    edge_drop : float, optional
+        The dropout rate on edges that controls the
+        messages received by each node. Default: ``0``.
+
+    Example
+    -------
+    >>> import dgl
+    >>> import numpy as np
+    >>> import mxnet as mx
+    >>> from dgl.nn import APPNPConv
+    >>>
+    >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3]))
+    >>> feat = mx.nd.ones((6, 10))
+    >>> conv = APPNPConv(k=3, alpha=0.5)
+    >>> conv.initialize(ctx=mx.cpu(0))
+    >>> res = conv(g, feat)
+    >>> res
+    [[1.         1.         1.         1.         1.         1.
+    1.         1.         1.         1.        ]
+    [1.         1.         1.         1.         1.         1.
+    1.         1.         1.         1.        ]
+    [1.         1.         1.         1.         1.         1.
+    1.         1.         1.         1.        ]
+    [1.0303301  1.0303301  1.0303301  1.0303301  1.0303301  1.0303301
+    1.0303301  1.0303301  1.0303301  1.0303301 ]
+    [0.86427665 0.86427665 0.86427665 0.86427665 0.86427665 0.86427665
+    0.86427665 0.86427665 0.86427665 0.86427665]
+    [0.5        0.5        0.5        0.5        0.5        0.5
+    0.5        0.5        0.5        0.5       ]]
+    <NDArray 6x10 @cpu(0)>
+    """
+
+    def __init__(self, k, alpha, edge_drop=0.0):
+        super(APPNPConv, self).__init__()
+        self._k = k
+        self._alpha = alpha
+        with self.name_scope():
+            self.edge_drop = nn.Dropout(edge_drop)
+
+    def forward(self, graph, feat):
+        r"""
+
+        Description
+        -----------
+        Compute APPNP layer.
+
+        Parameters
+        ----------
+        graph : DGLGraph
+            The graph.
+        feat : mx.NDArray
+            The input feature of shape :math:`(N, *)`. :math:`N` is the
+            number of nodes, and :math:`*` could be of any shape.
+
+        Returns
+        -------
+        mx.NDArray
+            The output feature of shape :math:`(N, *)` where :math:`*`
+            should be the same as input shape.
+        """
+        with graph.local_scope():
+            norm = mx.nd.power(
+                mx.nd.clip(
+                    graph.in_degrees().astype(feat.dtype),
+                    a_min=1,
+                    a_max=float("inf"),
+                ),
+                -0.5,
+            )
+            shp = norm.shape + (1,) * (feat.ndim - 1)
+            norm = norm.reshape(shp).as_in_context(feat.context)
+            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(
+                    nd.ones((graph.num_edges(), 1), ctx=feat.context)
+                )
+                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