Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution

Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Nguyen Quoc Viet Hung, Xiaofang Zhou, Lei Chen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume35
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • Dynamic graph embedding
  • Graph evolution
  • Edge timespan
  • Graph mining

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