Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks. It has achieved great success on many tasks of network analysis such as link prediction and node classification. Most of existing network embedding algorithms focus on how to learn static homogeneous networks effectively. However, networks in the real world are more complex, e.g., networks may consist of several types of nodes and edges (called heterogeneous information) and may vary over time in terms of dynamic nodes and edges (called evolutionary patterns). Limited work has been done for network embedding of dynamic heterogeneous networks as it is challenging to learn both evolutionary and heterogeneous information simultaneously. In this paper, we propose a novel dynamic heterogeneous network embedding method, termed as DyHATR, which uses hierarchical attention to learn heterogeneous information and incorporates recurrent neural networks with temporal attention to capture evolutionary patterns. We benchmark our method on four real-world datasets for the task of link prediction. Experimental results show that DyHATR significantly outperforms several state-of-the-art baselines.

中文翻译：

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使用带有时间 RNN 的分层注意为链路预测建模动态异构网络

网络嵌入旨在学习节点的低维表示，同时捕获网络的结构信息。它在链路预测、节点分类等许多网络分析任务上取得了巨大的成功。大多数现有的网络嵌入算法都专注于如何有效地学习静态同构网络。然而，现实世界中的网络更为复杂，例如，网络可能由多种类型的节点和边（称为异构信息）组成，并且可能随时间在动态节点和边（称为进化模式）方面发生变化。动态异构网络的网络嵌入工作有限，因为同时学习进化和异构信息具有挑战性。在本文中，我们提出了一种新颖的动态异构网络嵌入方法，称为 DyHATR，它使用分层注意力来学习异构信息，并结合具有时间注意力的循环神经网络来捕获进化模式。我们在四个真实世界的数据集上对我们的方法进行了基准测试，以完成链接预测的任务。实验结果表明，DyHATR 显着优于几个最先进的基线。