wehub-resource-sync
209 lines
6.4 KiB
Python
209 lines
6.4 KiB
Python
import dgl.function as fn
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import numpy as np
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import torch as th
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import torch.nn as nn
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class CAREConv(nn.Module):
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"""One layer of CARE-GNN."""
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def __init__(
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self,
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in_dim,
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out_dim,
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num_classes,
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edges,
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activation=None,
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step_size=0.02,
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):
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super(CAREConv, self).__init__()
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self.activation = activation
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self.step_size = step_size
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.num_classes = num_classes
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self.edges = edges
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self.dist = {}
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self.linear = nn.Linear(self.in_dim, self.out_dim)
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self.MLP = nn.Linear(self.in_dim, self.num_classes)
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self.p = {}
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self.last_avg_dist = {}
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self.f = {}
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self.cvg = {}
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for etype in edges:
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self.p[etype] = 0.5
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self.last_avg_dist[etype] = 0
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self.f[etype] = []
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self.cvg[etype] = False
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def _calc_distance(self, edges):
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# formula 2
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d = th.norm(
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th.tanh(self.MLP(edges.src["h"]))
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- th.tanh(self.MLP(edges.dst["h"])),
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1,
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1,
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)
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return {"d": d}
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def _top_p_sampling(self, g, p):
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# this implementation is low efficient
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# optimization requires dgl.sampling.select_top_p requested in issue #3100
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dist = g.edata["d"]
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neigh_list = []
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for node in g.nodes():
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edges = g.in_edges(node, form="eid")
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num_neigh = th.ceil(g.in_degrees(node) * p).int().item()
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neigh_dist = dist[edges]
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if neigh_dist.shape[0] > num_neigh:
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neigh_index = np.argpartition(
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neigh_dist.cpu().detach(), num_neigh
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)[:num_neigh]
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else:
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neigh_index = np.arange(num_neigh)
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neigh_list.append(edges[neigh_index])
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return th.cat(neigh_list)
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def forward(self, g, feat):
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with g.local_scope():
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g.ndata["h"] = feat
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hr = {}
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for i, etype in enumerate(g.canonical_etypes):
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g.apply_edges(self._calc_distance, etype=etype)
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self.dist[etype] = g.edges[etype].data["d"]
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sampled_edges = self._top_p_sampling(g[etype], self.p[etype])
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# formula 8
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g.send_and_recv(
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sampled_edges,
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fn.copy_u("h", "m"),
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fn.mean("m", "h_%s" % etype[1]),
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etype=etype,
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)
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hr[etype] = g.ndata["h_%s" % etype[1]]
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if self.activation is not None:
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hr[etype] = self.activation(hr[etype])
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# formula 9 using mean as inter-relation aggregator
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p_tensor = (
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th.Tensor(list(self.p.values())).view(-1, 1, 1).to(g.device)
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)
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h_homo = th.sum(th.stack(list(hr.values())) * p_tensor, dim=0)
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h_homo += feat
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if self.activation is not None:
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h_homo = self.activation(h_homo)
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return self.linear(h_homo)
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class CAREGNN(nn.Module):
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def __init__(
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self,
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in_dim,
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num_classes,
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hid_dim=64,
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edges=None,
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num_layers=2,
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activation=None,
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step_size=0.02,
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):
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super(CAREGNN, self).__init__()
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self.in_dim = in_dim
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self.hid_dim = hid_dim
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self.num_classes = num_classes
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self.edges = edges
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self.activation = activation
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self.step_size = step_size
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self.num_layers = num_layers
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self.layers = nn.ModuleList()
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if self.num_layers == 1:
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# Single layer
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self.layers.append(
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CAREConv(
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self.in_dim,
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self.num_classes,
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self.num_classes,
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self.edges,
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activation=self.activation,
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step_size=self.step_size,
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)
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)
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else:
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# Input layer
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self.layers.append(
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CAREConv(
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self.in_dim,
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self.hid_dim,
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self.num_classes,
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self.edges,
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activation=self.activation,
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step_size=self.step_size,
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)
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)
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# Hidden layers with n - 2 layers
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for i in range(self.num_layers - 2):
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self.layers.append(
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CAREConv(
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self.hid_dim,
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self.hid_dim,
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self.num_classes,
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self.edges,
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activation=self.activation,
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step_size=self.step_size,
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)
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)
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# Output layer
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self.layers.append(
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CAREConv(
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self.hid_dim,
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self.num_classes,
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self.num_classes,
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self.edges,
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activation=self.activation,
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step_size=self.step_size,
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)
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)
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def forward(self, graph, feat):
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# For full graph training, directly use the graph
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# formula 4
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sim = th.tanh(self.layers[0].MLP(feat))
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# Forward of n layers of CARE-GNN
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for layer in self.layers:
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feat = layer(graph, feat)
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return feat, sim
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def RLModule(self, graph, epoch, idx):
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for layer in self.layers:
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for etype in self.edges:
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if not layer.cvg[etype]:
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# formula 5
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eid = graph.in_edges(idx, form="eid", etype=etype)
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avg_dist = th.mean(layer.dist[etype][eid])
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# formula 6
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if layer.last_avg_dist[etype] < avg_dist:
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if layer.p[etype] - self.step_size > 0:
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layer.p[etype] -= self.step_size
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layer.f[etype].append(-1)
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else:
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if layer.p[etype] + self.step_size <= 1:
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layer.p[etype] += self.step_size
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layer.f[etype].append(+1)
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layer.last_avg_dist[etype] = avg_dist
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# formula 7
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if epoch >= 9 and abs(sum(layer.f[etype][-10:])) <= 2:
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layer.cvg[etype] = True
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