sage.py

"""GraphSAGE using DGL nn package

Inductive Representation Learning on Large Graphs
Paper: http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
Code: https://github.com/williamleif/graphsage-simple
Simple reference implementation of GraphSAGE.
"""
import torch
import torch.nn as nn
from dgl.nn.pytorch.conv import SAGEConv


class GraphSAGE(nn.Module):
    def __init__(self,
                 g,
                 in_feats,
                 n_hidden,
                 n_classes,
                 n_layers,
                 activation,
                 dropout,
                 aggregator_type):
        super(GraphSAGE, self).__init__()
        self.layers = nn.ModuleList()
        self.g = g

        # input layer
        self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
        # hidden layers
        for i in range(n_layers - 1):
            self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
        # output layer
        self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type, feat_drop=dropout, activation=None)) # activation None

    def forward(self, features):
        h = features
        for layer in self.layers:
            h = layer(self.g, h)
        return h