Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of such dynamics where the evolution of each local structure starts at different times and lasts for various durations. To maintain asynchronous structural evolutions within the graph, we innovatively formulate dynamic graphs as temporal edge sequences associated with joining time of vertices (ToV) and timespan of edges (ToE). Then, a time-aware Transformer is proposed to embed vertices' dynamic connections and ToEs into the learned vertex representations. Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information. Extensive evaluations on several datasets show that our approach outperforms the state-of-the-art in a wide range of graph mining tasks. At the same time, it is very efficient and scalable for embedding large-scale dynamic graphs.

翻译：动态图是指其结构随时间动态变化的图。尽管学习动态图的顶点表示（即嵌入）具有诸多优势，但现有工作仅将动态图视为顶点连接变化的序列，忽略了这种动态过程中至关重要的异步特性——每个局部结构的演化开始时间不同且持续时间各异。为捕捉图内的异步结构演化，我们创新性地将动态图形式化为与顶点加入时间（ToV）和边持续时间（ToE）相关联的时间边序列。随后，提出一种时间感知Transformer，将顶点的动态连接与ToE嵌入到学得的顶点表示中。同时，我们将每条边序列视为一个整体，并嵌入其首个顶点的ToV，以进一步编码时间敏感信息。在多个数据集上的广泛评估表明，我们的方法在广泛的图挖掘任务中优于现有最先进技术。此外，该方法在大规模动态图嵌入方面具有高效性和可扩展性。