import networkx as nx
import torch
import torch.nn.functional as F
from .. import BaseModel
from cogdl.layers import GATLayer
[docs]class DAEGC(BaseModel):
r"""The DAEGC model from the `"Attributed Graph Clustering: A Deep Attentional Embedding Approach"
<https://arxiv.org/abs/1906.06532>`_ paper
Args:
num_clusters (int) : Number of clusters.
T (int) : Number of iterations to recalculate P and Q
gamma (float) : Hyperparameter that controls two parts of the loss.
"""
[docs] @staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument("--num-features", type=int)
parser.add_argument("--hidden-size", type=int, default=256)
parser.add_argument("--embedding-size", type=int, default=16)
parser.add_argument("--num-heads", type=int, default=1)
parser.add_argument("--dropout", type=float, default=0)
parser.add_argument("--epochs", type=int, default=100)
parser.add_argument("--lr", type=float, default=0.001)
# parser.add_argument("--T", type=int, default=5)
parser.add_argument("--gamma", type=float, default=10)
# fmt: on
[docs] @classmethod
def build_model_from_args(cls, args):
return cls(
args.num_features, args.hidden_size, args.embedding_size, args.num_heads, args.dropout, args.num_clusters
)
def __init__(self, num_features, hidden_size, embedding_size, num_heads, dropout, num_clusters):
super(DAEGC, self).__init__()
self.hidden_size = hidden_size
self.num_heads = num_heads
self.embedding_size = embedding_size
self.dropout = dropout
self.num_clusters = num_clusters
self.att1 = GATLayer(num_features, hidden_size, attn_drop=dropout, alpha=0.2, nhead=num_heads)
self.att2 = GATLayer(hidden_size * num_heads, embedding_size, attn_drop=dropout, alpha=0.2, nhead=1)
self.cluster_center = torch.nn.Parameter(torch.FloatTensor(self.num_clusters))
[docs] def set_cluster_center(self, center):
self.cluster_center.data = center
[docs] def get_cluster_center(self):
return self.cluster_center.data.detach()
[docs] def forward(self, graph):
x = graph.x
x = F.dropout(x, p=self.dropout, training=self.training)
x = F.elu(self.att1(graph, x))
x = F.dropout(x, p=self.dropout, training=self.training)
x = F.elu(self.att2(graph, x))
return F.normalize(x, p=2, dim=1)
[docs] def get_2hop(self, edge_index):
r"""add 2-hop neighbors as new edges"""
G = nx.Graph()
edge_index = torch.stack(edge_index)
G.add_edges_from(edge_index.t().tolist())
H = nx.Graph()
for i in range(G.number_of_nodes()):
layers = dict(nx.bfs_successors(G, source=i, depth_limit=2))
for succ in layers:
for idx in layers[succ]:
H.add_edge(i, idx)
return torch.tensor(list(H.edges())).t()
[docs] def get_features(self, data):
return self.forward(data).detach()
[docs] def recon_loss(self, z, adj):
# print(torch.mm(z, z.t()), adj)
return F.binary_cross_entropy(F.sigmoid(torch.mm(z, z.t())), adj, reduction="sum")