hugegraph-ml/src/hugegraph_ml/models/appnp.py

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"""
Approximate personalized propagation of neural predictions (APPNP)

References
----------
Paper: https://arxiv.org/abs/1810.05997
Author's code: https://github.com/klicperajo/ppnp
DGL code: https://github.com/dmlc/dgl/tree/master/examples/pytorch/appnp
"""

from torch import nn

from dgl.nn.pytorch.conv import APPNPConv


class APPNP(nn.Module):
    def __init__(
        self,
        in_feats,
        hiddens,
        n_classes,
        activation,
        feat_drop,
        edge_drop,
        alpha,
        k,
    ):
        super(APPNP, self).__init__()
        self.layers = nn.ModuleList()
        # input layer
        self.layers.append(nn.Linear(in_feats, hiddens[0]))
        # hidden layers
        for i in range(1, len(hiddens)):
            self.layers.append(nn.Linear(hiddens[i - 1], hiddens[i]))
        # output layer
        self.layers.append(nn.Linear(hiddens[-1], n_classes))
        self.activation = activation
        if feat_drop:
            self.feat_drop = nn.Dropout(feat_drop)
        else:
            self.feat_drop = lambda x: x
        self.propagate = APPNPConv(k, alpha, edge_drop)
        self.reset_parameters()

        self.criterion = nn.CrossEntropyLoss()

    def reset_parameters(self):
        for layer in self.layers:
            layer.reset_parameters()

    def forward(self, graph, features):
        # prediction step
        h = features
        h = self.feat_drop(h)
        h = self.activation(self.layers[0](h))
        for layer in self.layers[1:-1]:
            h = self.activation(layer(h))
        h = self.layers[-1](self.feat_drop(h))
        # propagation step
        h = self.propagate(graph, h)
        return h

    def loss(self, logits, labels):
        return self.criterion(logits, labels)

    def inference(self, graph, feats):
        return self.forward(graph, feats)