基于图卷积与长短期记忆网络的动态网络表示学习模型

针对动态网络节点之间链路预测的准确率低和运行时间长的情况，提出了一种以降噪自编码器（dAE）为框架，结合图卷积网络（GCN）和长短期记忆（LSTM）网络的动态网络表示学习模型dynGAELSTM。首先，该模型的前端采用GCN捕获动态图节点的高阶图邻域的特征信息；其次，将提取到的信息输入dAE的编码层以获取低维特征向量，并在LSTM网络上获取动态网络的时空依赖特征；最后，经dAE的解码层重建预测图，并与真实图对比来构建损失函数，从而优化模型完成链路预测。理论分析和仿真实验表明，dynGAELSTM模型相较于预测性能第二的模型在三个数据集上的预测性能分别提升了0.79、1.19和3.13个百分点，模型的运行时间降低了0.92%和1.73%。可见dynGAELSTM模型在链路预测任务中相较于现有模型精度提升，复杂度降低。

Dynamic network representation learning model based on graph convolutional network and long short-term memory network

Concerning the low accuracy and long running time of link prediction between dynamic network nodes, a dynamic network representation learning model using denoising AutoEncoder (dAE) as the framework and combining with Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) network, named dynGAELSTM, was proposed. Firstly, the GCN was used in the front-end of this model to capture the feature information of the high-order graph neighborhood of the dynamic network nodes. Secondly, the extracted information was input into the coding layer of the dAE to obtain the low-dimensional feature vectors, and the spatio-temporal dependent features of the dynamic network were obtained on the LSTM network. Finally, a loss function was constructed by comparing the prediction map reconstructed through the decoding layer of the dAE with the real map, so as to optimize the model to complete the link prediction. Theoretical analysis and simulation experiments showed that compared with the model with the second-best prediction performance, the dynGAELSTM model had the prediction performance improved by 0.79, 1.19 and 3.13 percentage points respectively, and the running time reduced by 0.92% and 1.73% respectively. In summary, the dynGAELSTM model has higher accuracy and lower complexity in the link prediction tasks compared to the existing models.