Network embedding is a very important method for network data. However, most of the algorithms can only deal with static networks. In this paper, we propose an algorithm Recurrent Neural Network Embedding (RNNE) to deal with dynamic network, which can be typically divided into two categories: a) topologically evolving graphs whose nodes and edges will increase (decrease) over time; b) temporal graphs whose edges contain time information. In order to handle the changing size of dynamic networks, RNNE adds virtual node, which is not connected to any other nodes, to the networks and replaces it when new node arrives, so that the network size can be unified at different time. On the one hand, RNNE pays attention to the direct links between nodes and the similarity between the neighborhood structures of two nodes, trying to preserve the local and global network structure. On the other hand, RNNE reduces the influence of noise by transferring the previous embedding information. Therefore, RNNE can take into account both static and dynamic characteristics of the network.We evaluate RNNE on five networks and compare with several state-of-the-art algorithms. The results demonstrate that RNNE has advantages over other algorithms in reconstruction, classification and link predictions.

中文翻译：

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在线动态网络嵌入

网络嵌入是一种非常重要的网络数据方法。然而，大多数算法只能处理静态网络。在本文中，我们提出了一种算法循环神经网络嵌入（RNNE）来处理动态网络，它通常可以分为两类：a）节点和边会随着时间增加（减少）的拓扑演化图；b) 边包含时间信息的时间图。为了应对动态网络规模的变化，RNNE在网络中加入了不与其他任何节点相连的虚拟节点，并在新节点到来时替换它，使网络规模可以在不同时间统一。一方面，RNNE 关注节点之间的直接联系以及两个节点的邻域结构之间的相似性，试图保留本地和全球网络结构。另一方面，RNNE 通过传递之前的嵌入信息来减少噪声的影响。因此，RNNE 可以兼顾网络的静态和动态特性。我们在五个网络上评估 RNNE，并与几种最先进的算法进行比较。结果表明，RNNE 在重建、分类和链接预测方面优于其他算法。