Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation

no code implementations • 12 Apr 2023 • Yuxing Tian, Mingjie Zhu, Jiachi Luo, Song Li

This study focuses on long-term forecasting (LTF) on continuous-time dynamic graph networks (CTDGNs), which is important for real-world modeling.

Data Augmentation

M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data prediction

no code implementations • 27 Oct 2022 • Yuxing Tian, Zheng Liu, Yanwen Qu, Song Li, Jiachi Luo

This paper proposes a new GNN-oriented split federated learning method, named node {\bfseries M}asking and {\bfseries M}ulti-granularity {\bfseries M}essage passing-based Federated Graph Model (M$^3$FGM) for the above issues.

Federated Learning