no code implementations • 16 Dec 2021 • Linpu Jiang, Ke-Jia Chen, Jingqiang Chen
Specifically, a novel temporal subgraph sampling strategy is firstly proposed, which takes each node of the dynamic graph as the central node and uses both neighborhood structures and edge timestamps to sample the corresponding temporal subgraph.
1 code implementation • 24 Feb 2021 • Ke-Jia Chen, Jiajun Zhang, Linpu Jiang, Yunyun Wang, Yuxuan Dai
This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph.