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

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"""
Cluster-GCN

References
----------
Paper: https://arxiv.org/abs/1905.07953
Author's code: https://github.com/google-research/google-research/tree/master/cluster_gcn
DGL code: https://github.com/dmlc/dgl/tree/master/examples/pytorch/cluster_gcn
"""

from torch import nn
import torch.nn.functional as F

import dgl.nn as dglnn

class SAGE(nn.Module):
    # pylint: disable=E1101
    def __init__(self, in_feats, n_hidden, n_classes):
        super().__init__()
        self.layers = nn.ModuleList()
        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, "mean"))
        self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, "mean"))
        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, "mean"))
        self.dropout = nn.Dropout(0.5)

    def forward(self, sg, x):
        h = x
        for l, layer in enumerate(self.layers):
            h = layer(sg, h)
            if l != len(self.layers) - 1:
                h = F.relu(h)
                h = self.dropout(h)
        return h

    def loss(self, logits, labels):
        return nn.CrossEntropyLoss()(logits, labels)

    def inference(self, sg, x):
        return self.forward(sg, x)