GCN-LSTM网络代码

GCN-LSTM网络结合了图卷积神经网络(GCN)和长短时记忆网络(LSTM)，主要用于处理图形数据中的序列信息。这种架构通常在社交网络分析、推荐系统以及自然语言处理等任务中展现优势。以下是简单概述和一个简化的伪代码示例：

首先，你需要导入所需的库，如TensorFlow或PyTorch，并可能使用Graph Nets库来构建图相关的部分。

import tensorflow as tf
from tensorflow.keras.layers import GraphConv, LSTM, Dense

# 假设我们有一个邻接矩阵A和节点特征X
adjacency_matrix = ... # shape [num_nodes, num_nodes]
node_features = ...   # shape [num_nodes, node_feature_size]

class GCN_LSTM(tf.keras.Model):
    def __init__(self, hidden_units, num_layers):
        super(GCN_LSTM, self).__init__()
        self.gcn_layers = [GraphConv(hidden_units, activation='relu') for _ in range(num_layers)]
        self.lstm_layer = LSTM(hidden_units)
        self.dense_output = Dense(1)

    def call(self, inputs):
        adj, node_features = inputs
        for gcn in self.gcn_layers:
            node_features = gcn([adj, node_features])
        node_sequences = tf.unstack(node_features, axis=0)  # 对每个时间步拆分
        lstm_outputs = self.lstm_layer(node_sequences)  # 循环LSTM
        return self.dense_output(lstm_outputs[-1])  # 取最后一个时间步作为最终预测

model = GCN_LSTM(hidden_units=64, num_layers=2)
inputs = (adjacency_matrix, node_features)
predictions = model(inputs)