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models.py
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models.py
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# coding: utf-8
import torch
import torch.nn as nn
from layers import CoreDiffusion, MLP
# K-core diffusion network
class CDN(nn.Module):
input_dim: int
hidden_dim: int
output_dim: int
diffusion_num: int
bias: bool
rnn_type: str
def __init__(self, input_dim, hidden_dim, output_dim, diffusion_num, bias=True, rnn_type='GRU'):
super(CDN, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.diffusion_num = diffusion_num
self.bias = bias
self.rnn_type = rnn_type
if diffusion_num == 1:
self.diffusion_list = nn.ModuleList()
self.diffusion_list.append(CoreDiffusion(input_dim, output_dim, bias=bias, rnn_type=rnn_type))
elif diffusion_num > 1:
self.diffusion_list = nn.ModuleList()
self.diffusion_list.append(CoreDiffusion(input_dim, hidden_dim, bias=bias, rnn_type=rnn_type))
for i in range(diffusion_num - 2):
self.diffusion_list.append(CoreDiffusion(hidden_dim, hidden_dim, bias=bias, rnn_type=rnn_type))
self.diffusion_list.append(CoreDiffusion(hidden_dim, output_dim, bias=bias, rnn_type=rnn_type))
else:
raise ValueError("number of layers should be positive!")
# x: node feature tensor
# adj_list: k-core subgraph adj list
def forward(self, x, adj_list):
for i in range(self.diffusion_num):
x = self.diffusion_list[i](x, adj_list)
return x
# MLP classifier
class MLPClassifier(nn.Module):
input_dim: int
hidden_dim: int
output_dim: int
layer_num: int
duration: int
bias: bool
activate_type: str
def __init__(self, input_dim, hidden_dim, output_dim, layer_num, duration, bias=True, activate_type='N'):
super(MLPClassifier, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.layer_num = layer_num
self.duration = duration
self.bias = bias
self.activate_type = activate_type
self.mlp_list = nn.ModuleList()
for i in range(self.duration):
self.mlp_list.append(MLP(input_dim, hidden_dim, output_dim, layer_num, bias=bias, activate_type=activate_type))
def forward(self, x, batch_indices=None):
if isinstance(x, list) or len(x.size()) == 3: # list or 3D tensor(GCRN, CTGCN output)
timestamp_num = len(x)
output_list = []
for i in range(timestamp_num):
output_list.append(self.mlp_classifier(x[i], batch_indices[i]))
return output_list
return self.mlp_classifier(x, batch_indices)
def mlp_classifier(self, x, batch_indices=None):
# x is a tensor
embedding_mat = x[batch_indices] if batch_indices is not None else x
x = self.mlp_list[0](embedding_mat)
return x
# This class supports inner product edge features!
class InnerProduct(nn.Module):
reduce: bool
def __init__(self, reduce=True):
super(InnerProduct, self).__init__()
self.reduce = reduce
def forward(self, x, edge_index):
if isinstance(x, list) or len(x.size()) == 3: # list or 3D tensor(GCRN, CTGCN output)
timestamp_num = len(x)
output_list = []
for i in range(timestamp_num):
embedding_mat = x[i]
edge_mat = edge_index[i]
output_list.append(self.inner_product(embedding_mat, edge_mat))
return output_list
# x is a tensor
return self.inner_product(x, edge_index)
def inner_product(self, x, edge_index):
# x is a tensor
assert edge_index.shape[0] == 2
edge_index = edge_index.transpose(0, 1) # [edge_num, 2]
embedding_i = x[edge_index[:, 0]]
embedding_j = x[edge_index[:, 1]]
if self.reduce:
return torch.sum(embedding_i * embedding_j, dim=1)
return embedding_i * embedding_j
class EdgeClassifier(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, layer_num, duration, bias=True, activate_type='N'):
super(EdgeClassifier, self).__init__()
self.conv = InnerProduct(reduce=False)
self.classifier = MLPClassifier(input_dim, hidden_dim, output_dim, layer_num, duration, bias=bias, activate_type=activate_type)
def forward(self, x, edge_index):
x = self.conv(x, edge_index)
return self.classifier(x)
# K-core based graph convolutional network
class CGCN(nn.Module):
input_dim: int
hidden_dim: int
output_dim: int
trans_num: int
diffusion_num: int
bias: bool
rnn_type: str
model_type: str
trans_activate_type: str
method_name: str
def __init__(self, input_dim, hidden_dim, output_dim, trans_num, diffusion_num, bias=True, rnn_type='GRU', model_type='C', trans_activate_type='L'):
super(CGCN, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.trans_num = trans_num
self.diffusion_num = diffusion_num
self.bias = bias
self.rnn_type = rnn_type
self.model_type = model_type
self.trans_activate_type = trans_activate_type
self.method_name = 'CGCN' + '-' + model_type
assert self.model_type in ['C', 'S']
assert self.trans_activate_type in ['L', 'N']
if self.model_type == 'C':
# self.mlp = nn.Linear(input_dim, hidden_dim, bias=bias)
self.mlp = MLP(input_dim, hidden_dim, hidden_dim, trans_num, bias=bias, activate_type=trans_activate_type)
self.duffision = CDN(hidden_dim, output_dim, output_dim, diffusion_num, rnn_type=rnn_type)
else:
self.mlp = MLP(input_dim, hidden_dim, output_dim, trans_num, bias=bias, activate_type=trans_activate_type)
self.duffision = CDN(output_dim, output_dim, output_dim, diffusion_num, rnn_type=rnn_type)
def forward(self, x, adj):
if isinstance(x, list):
timestamp_num = len(x)
embedding_list, structure_list = [], []
if self.model_type == 'C':
for i in range(timestamp_num):
embedding_mat = self.cgcn(x[i], adj[i])
embedding_list.append(embedding_mat)
return embedding_list
else:
for i in range(timestamp_num):
embedding_mat, structure_mat = self.cgcn(x[i], adj[i])
embedding_list.append(embedding_mat)
structure_list.append(structure_mat)
return embedding_list, structure_list
return self.cgcn(x, adj)
def cgcn(self, x, adj):
trans = self.mlp(x)
x = self.duffision(trans, adj)
if self.model_type == 'S':
return x, trans
return x
# K-core based temporal graph convolutional network
class CTGCN(nn.Module):
input_dim: int
hidden_dim: int
output_dim: int
duration: int
trans_num: int
diffusion_num: int
bias: bool
rnn_type: str
model_type: str
trans_activate_type: str
method_name: str
def __init__(self, input_dim, hidden_dim, output_dim, trans_num, diffusion_num, duration, bias=True, rnn_type='GRU', model_type='C', trans_activate_type='L'):
super(CTGCN, self).__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.rnn_type = rnn_type
self.model_type = model_type
self.trans_activate_type = trans_activate_type
self.method_name = 'CTGCN' + '-' + model_type
assert self.model_type in ['C', 'S']
assert self.trans_activate_type in ['L', 'N']
self.duration = duration
self.trans_num = trans_num
self.diffusion_num = diffusion_num
self.bias = bias
self.mlp_list = nn.ModuleList()
self.duffision_list = nn.ModuleList()
for i in range(self.duration):
if self.model_type == 'C':
self.mlp_list.append(MLP(input_dim, hidden_dim, hidden_dim, trans_num, bias=bias, activate_type=trans_activate_type))
self.duffision_list.append(CDN(hidden_dim, output_dim, output_dim, diffusion_num, rnn_type=rnn_type))
else: # model_type == 'S'
self.mlp_list.append(MLP(input_dim, hidden_dim, output_dim, trans_num, bias=bias, activate_type=trans_activate_type))
self.duffision_list.append(CDN(output_dim, output_dim, output_dim, diffusion_num, rnn_type=rnn_type))
assert self.rnn_type in ['LSTM', 'GRU']
if self.rnn_type == 'LSTM':
self.rnn = nn.LSTM(output_dim, output_dim, num_layers=1, bias=bias, batch_first=True)
else:
self.rnn = nn.GRU(output_dim, output_dim, num_layers=1, bias=bias, batch_first=True)
self.norm = nn.LayerNorm(output_dim)
def forward(self, x_list, adj_list):
time_num = len(x_list)
hx_list, trans_list = [], []
for i in range(time_num):
x = self.mlp_list[i](x_list[i])
trans_list.append(x)
x = self.duffision_list[i](x, adj_list[i])
hx_list.append(x)
hx = torch.stack(hx_list).transpose(0, 1)
out, _ = self.rnn(hx)
out = self.norm(out).transpose(0, 1)
if self.model_type == 'C':
return out
return out, trans_list