基于深度神经网络和门控循环单元的动态图表示学习方法

学习图中节点的潜在向量表示是一项重要且普遍存在的任务，旨在捕捉图中节点的各种属性。大量工作证明静态图表示已经能够学习到节点的部分信息，然而，真实世界的图是随着时间的推移而演变的。为了解决多数动态网络算法不能有效保留节点邻域结构和时态信息的问题，提出了基于深度神经网络（DNN）和门控循环单元（GRU）的动态网络表示学习方法DynAEGRU。该方法以自编码器作为框架，其中的编码器首先用DNN聚集邻域信息以得到低维特征向量，然后使用GRU网络提取节点时态信息，最后用解码器重构邻接矩阵并将其与真实图对比来构建损失。通过与几种静态图和动态图表示学习算法在3个数据集上进行实验分析，结果表明DynAEGRU具有较好的性能增益。

Dynamic graph representation learning method based on deep neural network and gated recurrent unit

Learning the latent vector representations of nodes in the graph is an important and ubiquitous task， which aims to capture various attributes of the nodes in the graph. A lot of work demonstrates that static graph representation learning can learn part of the node information； however， real-world graphs evolve over time. In order to solve the problem that most dynamic network algorithms cannot effectively retain node neighborhood structure and temporal information， a dynamic network representation learning method based on Deep Neural Network （DNN） and Gated Recurrent Unit （GRU）， namely DynAEGRU， was proposed. With Auto-Encoder （AE） as the framework of the DynAEGRU， the neighborhood information was aggregated by encoder with a DNN to obtain low-dimensional feature vectors， then the node temporal information was extracted by a GRU network，finally， the adjacency matrix was reconstructed by the decoder and compared with the real graph to construct the loss. Experimental results on three real-word datasets show that DynAEGRU method has better performance gain compared to several static and dynamic graph representation learning algorithms.