Heterogeneous temporal network embedding aims to learn each node of different types of a heterogeneous temporal network in each snapshot into a low-dimensional vector representation, which can be used for various network analysis tasks such as node classification and relationship prediction. Our work proposes a novel heterogeneous temporal graph neural network embedding framework (TemporalHAN) based on hierarchical attention using a temporal convolutional network (TCN). In particular, we introduce node-level and semantic-level attention into heterogeneous graph neural networks to identify the importance of different levels between nodes. For each snapshot, we first utilise a new random walk algorithm (NRWA) to collect strongly connected heterogeneous neighbours for each node of different types and group them by node types. In addition, the algorithm utilises a damping factor to ensure that the more recent snapshots allocate more random walk steps. We then utilise node-level and semantic-level attention to learn the importance between a node and its random walk neighbour for a specific node type and learn the importance of different node-type for this node, respectively. Finally, we adopt TCN to capture the evolution information between snapshots. Experimental results on relationship prediction and node classification reveal that the TemporalHAN is competitive against diverse state-of-the-art approaches. Our code is available at .

中文翻译：

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TemporalHAN：基于分层注意力的异构时间网络嵌入

异构时间网络嵌入旨在将每个快照中不同类型的异构时间网络的每个节点学习为低维向量表示，可用于节点分类和关系预测等各种网络分析任务。我们的工作提出了一种新颖的异构时间图神经网络嵌入框架（TemporalHAN），该框架基于使用时间卷积网络（TCN）的分层注意力。特别是，我们将节点级和语义级注意力引入异构图神经网络中，以识别节点之间不同级别的重要性。对于每个快照，我们首先利用新的随机游走算法（NRWA）来收集不同类型的每个节点的强连接异构邻居，并按节点类型对它们进行分组。此外，该算法利用阻尼因子来确保较新的快照分配更多的随机游走步骤。然后，我们利用节点级和语义级注意力来学习特定节点类型的节点与其随机游走邻居之间的重要性，并分别学习不同节点类型对该节点的重要性。最后，我们采用TCN来捕获快照之间的演化信息。关系预测和节点分类的实验结果表明，TemporalHAN 与多种最先进的方法相比具有竞争力。我们的代码可在 .