activity
20182023
most citedGMAN: A Graph Multi-Attention Network for Traffic Prediction

87 citations · 112 across the 12 of their papers we have counts for

collaborators

23 papers

cs.IR2023

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models

Rui Zhang, Yixin Su, Bayu Distiawan Trisedya +4

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…

cs.CL20221 cited

TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs

Rui Zhang, Xiaoyan Zhao, Bayu Distiawan Trisedya +3

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-bas…

cs.DB2021

SkyCell: A Space-Pruning Based Parallel Skyline Algorithm

Chuanwen Li, Yu Gu, Jianzhong Qi +1

Skyline computation is an essential database operation that has many applications in multi-criteria decision making scenarios such as recommender systems. Existing algorithms have…

cs.DB20214 cited

Sub-trajectory Similarity Join with Obfuscation

Yanchuan Chang, Jianzhong Qi, Egemen Tanin +2

User trajectory data is becoming increasingly accessible due to the prevalence of GPS-equipped devices such as smartphones. Many existing studies focus on querying trajectories tha…

cs.LG2021

Fast, Accurate and Interpretable Time Series Classification Through Randomization

Nestor Cabello, Elham Naghizade, Jianzhong Qi +1

Time series classification (TSC) aims to predict the class label of a given time series, which is critical to a rich set of application areas such as economics and medicine. State-…

cs.LG2021

Federated Learning with Fair Averaging

Zheng Wang, Xiaoliang Fan, Jianzhong Qi +3

Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the…