34 citations · 45 across the 6 of their papers we have counts for
6 papers
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning
Jiawei Jiang, Yifan Yang, Jingyuan Wang +1
The electronic map plays a crucial role in geographic information systems, serving various urban managerial scenarios and daily life services. Developing effective Map Entity Repre…
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
Yuxiang Wang, Xiao Yan, Chuang Hu +5
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features. Contrastive Learn…
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
Qiang Huang, Jiawei Jiang, Xi Susie Rao +10
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGN…
Continuous Trajectory Generation Based on Two-Stage GAN
Wenjun Jiang, Wayne Xin Zhao, Jingyuan Wang +1
Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geo…
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates
Fangcheng Fu, Xupeng Miao, Jiawei Jiang +2
Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with p…
Efficient Diversity-Driven Ensemble for Deep Neural Networks
Wentao Zhang, Jiawei Jiang, Yingxia Shao +1
The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost…