Method apply_edges

tutorials/blitz/4_link_predict.py:239–257

Computes a scalar score for each edge of the given graph. Parameters ---------- edges : Has three members ``src``, ``dst`` and ``data``, each of which is a dictionary representing the features of the source nodes, the destination

(self, edges)

        self.W2 = nn.Linear(h_feats, 1)

    def apply_edges(self, edges):
        """
        Computes a scalar score for each edge of the given graph.

        Parameters
        ----------
        edges :
            Has three members ``src``, ``dst`` and ``data``, each of
            which is a dictionary representing the features of the
            source nodes, the destination nodes, and the edges
            themselves.

        Returns
        -------
        dict
            A dictionary of new edge features.
        """
        h = torch.cat([edges.src["h"], edges.dst["h"]], 1)
        return {"score": self.W2(F.relu(self.W1(h))).squeeze(1)}

    def forward(self, g, h):
        with g.local_scope():