Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
- URL: http://arxiv.org/abs/2207.00594v1
- Date: Fri, 1 Jul 2022 15:32:56 GMT
- Title: Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
- Authors: Yu Yang, Hongzhi Yin, Jiannong Cao, Tong Chen, Quoc Viet Hung Nguyen,
Xiaofang Zhou and Lei Chen
- Abstract summary: Existing works merely view a dynamic graph as a sequence of changes.
We formulate dynamic graphs as temporal edge sequences associated with joining time of.
vertex and timespan of edges.
A time-aware Transformer is proposed to embed.
vertex' dynamic connections and ToEs into the learned.
vertex representations.
- Score: 60.695162101159134
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
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