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.

该论文提出了一种用于动态图的时间感知变压器来嵌入顶点表示的方法，该方法使用时间边缘序列来维护异步结构演变，并在多个数据集上展示了其在图挖掘任务中的优越性能。