GCN项目实战1-SimGNN

SimGNN：快速图相似度计算的神经网络方法

原论文名称：SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
代码链接：https://github.com/benedekrozemberczki/SimGNN

1. 数据

例子：

{"graph_1": [[0, 8], [0, 9], [0, 2], [0, 3], [0, 11], [1, 2], [1, 3], [1, 5], [1, 6], [1, 7], [2, 3], [2, 5], [2, 6], [2, 7], [2, 8], [2, 10], [2, 11], [3, 5], [3, 7], [3, 8], [3, 10], [3, 11], [4, 9], [4, 10], [4, 5], [4, 6], [4, 7], [5, 7], [5, 8], [5, 11], [6, 7], [6, 8], [6, 11], [7, 8], [7, 10], [7, 11], [8, 9], [10, 11]], "ged": 32, "graph_2": [[0, 1], [0, 2], [0, 4], [1, 8], [1, 10], [1, 2], [1, 7], [2, 4], [2, 7], [2, 9], [2, 11], [3, 10], [3, 11], [3, 5], [3, 6], [3, 7], [4, 9], [4, 11], [5, 8], [5, 9], [5, 6], [6, 9], [7, 9], [7, 10], [7, 11], [8, 9], [8, 10], [9, 10], [9, 11], [10, 11]], "labels_2": ["3", "5", "6", "5", "4", "4", "3", "6", "4", "8", "6", "6"], "labels_1": ["5", "5", "9", "8", "5", "7", "6", "9", "7", "3", "5", "7"]}

一个样本包含五个属性：
graph_1：graph1的邻接矩阵
graph_2：graph2的邻接矩阵
labels_1：graph1的特征矩阵
labels_2：graph2的特征矩阵
ged：图相似度

2. 模型

2.1 python文件功能介绍

layers.py	-- 包含注意力机制层和Neural Tensor Network层的实现
simgnn.py   	-- 模型实现
utils.py  -- 一些辅助函数的实现
param_parser.py   -- 参数
main.py       -- 主函数--filters-1             INT         Number of filter in 1st GCN layer.       Default is 128.
--filters-2             INT         Number of filter in 2nd GCN layer.       Default is 64. 
--filters-3             INT         Number of filter in 3rd GCN layer.       Default is 32.
--tensor-neurons        INT         Neurons in tensor network layer.         Default is 16.
--bottle-neck-neurons   INT         Bottle neck layer neurons.               Default is 16.
--bins                  INT         Number of histogram bins.                Default is 16.
--batch-size            INT         Number of pairs processed per batch.     Default is 128. 
--epochs                INT         Number of SimGNN training epochs.        Default is 5.
--dropout               FLOAT       Dropout rate.                            Default is 0.5.
--learning-rate         FLOAT       Learning rate.                           Default is 0.001.
--weight-decay          FLOAT       Weight decay.                            Default is 10^-5.
--histogram             BOOL        Include histogram features.              Default is False.

2.2 重要函数和类的实现

1. 注意力机制层

• __init__初始化函数

功能：
导入需要的参数：args；
设置参数矩阵，形状为[self.args.filters_3,self.args.filters_3]：调用setup_weights()，
初始化参数矩阵的值：调用init_parameters()

def __init__(self, args):""":param args: Arguments object."""super(AttentionModule, self).__init__()self.args = argsself.setup_weights()self.init_parameters()

• setup_weights函数

def setup_weights(self):"""Defining weights."""self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.args.filters_3,self.args.filters_3))

• init_parameters函数

def init_parameters(self):"""Initializing weights."""torch.nn.init.xavier_uniform_(self.weight_matrix)

• forward函数

'''
key: embedding
value: embedding
query: embedding_i
'''
def forward(self, embedding): # embedding形状：[graph_node_num, self.args.filters_3]"""Making a forward propagation pass to create a graph level representation.:param embedding: Result of the GCN.:return representation: A graph level representation vector."""global_context = torch.mean(torch.matmul(embedding, self.weight_matrix), dim=0) # [1,self.args.filters_3]transformed_global = torch.tanh(global_context) # [1, self.args.filters_3]sigmoid_scores = torch.sigmoid(torch.mm(embedding, transformed_global.view(-1, 1))) # [graph_node_num, 1]representation = torch.mm(torch.t(embedding), sigmoid_scores) # [self.args.filters_3, 1]return representation

整体代码：

class AttentionModule(torch.nn.Module):"""SimGNN Attention Module to make a pass on graph."""def __init__(self, args):""":param args: Arguments object."""super(AttentionModule, self).__init__()self.args = argsself.setup_weights()self.init_parameters()def setup_weights(self):"""Defining weights."""self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.args.filters_3,self.args.filters_3))def init_parameters(self):"""Initializing weights."""torch.nn.init.xavier_uniform_(self.weight_matrix)def forward(self, embedding): """Making a forward propagation pass to create a graph level representation.:param embedding: Result of the GCN.:return representation: A graph level representation vector."""global_context = torch.mean(torch.matmul(embedding, self.weight_matrix), dim=0)transformed_global = torch.tanh(global_context)sigmoid_scores = torch.sigmoid(torch.mm(embedding, transformed_global.view(-1, 1)))representation = torch.mm(torch.t(embedding), sigmoid_scores)return representation

2. Neural Tensor Network层

class TenorNetworkModule(torch.nn.Module):"""SimGNN Tensor Network module to calculate similarity vector."""def __init__(self, args):""":param args: Arguments object."""super(TenorNetworkModule, self).__init__()self.args = argsself.setup_weights()self.init_parameters()def setup_weights(self):"""Defining weights."""self.weight_matrix = torch.nn.Parameter(torch.Tensor(self.args.filters_3,self.args.filters_3,self.args.tensor_neurons))self.weight_matrix_block = torch.nn.Parameter(torch.Tensor(self.args.tensor_neurons,2*self.args.filters_3))self.bias = torch.nn.Parameter(torch.Tensor(self.args.tensor_neurons, 1))def init_parameters(self):"""Initializing weights."""torch.nn.init.xavier_uniform_(self.weight_matrix)torch.nn.init.xavier_uniform_(self.weight_matrix_block)torch.nn.init.xavier_uniform_(self.bias)def forward(self, embedding_1, embedding_2):# embedding_1:[self.args.filters_3, 1]# embedding_2:[self.args.filters_3, 1]"""Making a forward propagation pass to create a similarity vector.:param embedding_1: Result of the 1st embedding after attention.:param embedding_2: Result of the 2nd embedding after attention.:return scores: A similarity score vector."""scoring = torch.mm(torch.t(embedding_1), self.weight_matrix.view(self.args.filters_3, -1)) # [1, self.args.filters_3*self.args.tensor_neurons]scoring = scoring.view(self.args.filters_3, self.args.tensor_neurons) # [self.args.filters_3, self.args.tensor_neurons]scoring = torch.mm(torch.t(scoring), embedding_2) # [self.args.tensor_neurons, 1]combined_representation = torch.cat((embedding_1, embedding_2)) # [2*self.args.filters_3, 1]block_scoring = torch.mm(self.weight_matrix_block, combined_representation) # [self.args.tensor_neurons, 1]scores = torch.nn.functional.relu(scoring + block_scoring + self.bias) # [self.args.tensor_neurons, 1]return scores

3. SimGNN模型

• __init__初始化函数：

功能：导入需要用到的参数：args，label数量：number_of_labels，构建模型：调用setup_layers函数

def __init__(self, args, number_of_labels):""":param args: Arguments object.:param number_of_labels: Number of node labels."""super(SimGNN, self).__init__()self.args = argsself.number_labels = number_of_labels # 存放label种类数量self.setup_layers()

• calculate_bottleneck_features函数：

功能：是否要加上histogram层（下半部分）提取的embedding

def calculate_bottleneck_features(self):"""Deciding the shape of the bottleneck layer."""if self.args.histogram == True:self.feature_count = self.args.tensor_neurons + self.args.binselse:self.feature_count = self.args.tensor_neurons

• setup_layers函数：

功能：构建模型，包括三个图卷积层，自注意力机制层，Neural Tensor Network层，两个线性层，最后输出一个预测的值。

def setup_layers(self):"""Creating the layers."""self.calculate_bottleneck_features()self.convolution_1 = GCNConv(self.number_labels, self.args.filters_1)self.convolution_2 = GCNConv(self.args.filters_1, self.args.filters_2)self.convolution_3 = GCNConv(self.args.filters_2, self.args.filters_3)self.attention = AttentionModule(self.args)self.tensor_network = TenorNetworkModule(self.args)self.fully_connected_first = torch.nn.Linear(self.feature_count,self.args.bottle_neck_neurons)self.scoring_layer = torch.nn.Linear(self.args.bottle_neck_neurons, 1)

• calculate_histogram函数：

def calculate_histogram(self, abstract_features_1, abstract_features_2):"""Calculate histogram from similarity matrix.:param abstract_features_1: Feature matrix for graph 1.:param abstract_features_2: Feature matrix for graph 2.:return hist: Histsogram of similarity scores."""scores = torch.mm(abstract_features_1, abstract_features_2).detach()scores = scores.view(-1, 1)hist = torch.histc(scores, bins=self.args.bins)hist = hist/torch.sum(hist)hist = hist.view(1, -1)return hist

• convolutional_pass函数：

def convolutional_pass(self, edge_index, features):"""Making convolutional pass.:param edge_index: Edge indices.:param features: Feature matrix.:return features: Absstract feature matrix."""features = self.convolution_1(features, edge_index)features = torch.nn.functional.relu(features)features = torch.nn.functional.dropout(features,p=self.args.dropout,training=self.training)features = self.convolution_2(features, edge_index)features = torch.nn.functional.relu(features)features = torch.nn.functional.dropout(features,p=self.args.dropout,training=self.training)features = self.convolution_3(features, edge_index)return features

• forward函数：

功能：运行神经网络，预测结果

def forward(self, data):"""Forward pass with graphs.:param data: Data dictiyonary.:return score: Similarity score."""edge_index_1 = data["edge_index_1"]edge_index_2 = data["edge_index_2"]features_1 = data["features_1"]features_2 = data["features_2"]# 图卷积的计算abstract_features_1 = self.convolutional_pass(edge_index_1, features_1) # [graph1_num_node,self.args.filters_3]abstract_features_2 = self.convolutional_pass(edge_index_2, features_2) # [graph2_num_node,self.args.filters_3]# 计算histogramif self.args.histogram == True:hist = self.calculate_histogram(abstract_features_1,torch.t(abstract_features_2))# 使用注意力机制层pooled_features_1 = self.attention(abstract_features_1)pooled_features_2 = self.attention(abstract_features_2)scores = self.tensor_network(pooled_features_1, pooled_features_2) # [self.args.tensor_neurons, 1]scores = torch.t(scores) # [1,self.args.tensor_neurons]# 合并注意力机制层和Neural Tensor Network层提取的特征if self.args.histogram == True:scores = torch.cat((scores, hist), dim=1).view(1, -1) # [1, self.feature_count]# 获得预测分数，由于标准答案使用了归一化，所以最后要过一下sigmoid层scores = torch.nn.functional.relu(self.fully_connected_first(scores)) # [1, self.args.bottle_neck_neurons]score = torch.sigmoid(self.scoring_layer(scores)) # self.scoring_layer(scores): [1,1]return score

整体代码：

class SimGNN(torch.nn.Module):"""SimGNN: A Neural Network Approach to Fast Graph Similarity Computationhttps://arxiv.org/abs/1808.05689"""def __init__(self, args, number_of_labels):""":param args: Arguments object.:param number_of_labels: Number of node labels."""super(SimGNN, self).__init__()self.args = argsself.number_labels = number_of_labelsself.setup_layers()def calculate_bottleneck_features(self):"""Deciding the shape of the bottleneck layer."""if self.args.histogram == True:self.feature_count = self.args.tensor_neurons + self.args.binselse:self.feature_count = self.args.tensor_neuronsdef setup_layers(self):"""Creating the layers."""self.calculate_bottleneck_features()self.convolution_1 = GCNConv(self.number_labels, self.args.filters_1)self.convolution_2 = GCNConv(self.args.filters_1, self.args.filters_2)self.convolution_3 = GCNConv(self.args.filters_2, self.args.filters_3)self.attention = AttentionModule(self.args)self.tensor_network = TenorNetworkModule(self.args)self.fully_connected_first = torch.nn.Linear(self.feature_count,self.args.bottle_neck_neurons)self.scoring_layer = torch.nn.Linear(self.args.bottle_neck_neurons, 1)def calculate_histogram(self, abstract_features_1, abstract_features_2):"""Calculate histogram from similarity matrix.:param abstract_features_1: Feature matrix for graph 1.:param abstract_features_2: Feature matrix for graph 2.:return hist: Histsogram of similarity scores."""scores = torch.mm(abstract_features_1, abstract_features_2).detach()scores = scores.view(-1, 1)hist = torch.histc(scores, bins=self.args.bins)hist = hist/torch.sum(hist)hist = hist.view(1, -1)return histdef convolutional_pass(self, edge_index, features):"""Making convolutional pass.:param edge_index: Edge indices.:param features: Feature matrix.:return features: Absstract feature matrix."""features = self.convolution_1(features, edge_index)features = torch.nn.functional.relu(features)features = torch.nn.functional.dropout(features,p=self.args.dropout,training=self.training)features = self.convolution_2(features, edge_index)features = torch.nn.functional.relu(features)features = torch.nn.functional.dropout(features,p=self.args.dropout,training=self.training)features = self.convolution_3(features, edge_index)return featuresdef forward(self, data):"""Forward pass with graphs.:param data: Data dictiyonary.:return score: Similarity score."""edge_index_1 = data["edge_index_1"]edge_index_2 = data["edge_index_2"]features_1 = data["features_1"]features_2 = data["features_2"]# 图卷积的计算abstract_features_1 = self.convolutional_pass(edge_index_1, features_1) # [graph1_num_node,self.args.filters_3]abstract_features_2 = self.convolutional_pass(edge_index_2, features_2) # [graph2_num_node,self.args.filters_3]# 计算histogramif self.args.histogram == True:hist = self.calculate_histogram(abstract_features_1,torch.t(abstract_features_2))# 使用注意力机制层pooled_features_1 = self.attention(abstract_features_1)pooled_features_2 = self.attention(abstract_features_2)scores = self.tensor_network(pooled_features_1, pooled_features_2) # [self.args.tensor_neurons, 1]scores = torch.t(scores) # [1,self.args.tensor_neurons]# 合并注意力机制层和Neural Tensor Network层提取的特征if self.args.histogram == True:scores = torch.cat((scores, hist), dim=1).view(1, -1) # [1, self.feature_count]# 获得预测分数，由于标准答案使用了归一化，所以最后要过一下sigmoid层scores = torch.nn.functional.relu(self.fully_connected_first(scores)) # [1, self.args.bottle_neck_neurons]score = torch.sigmoid(self.scoring_layer(scores)) # self.scoring_layer(scores): [1,1]return score