activity
20202025
most citedTree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning

4 citations · 15 across the 9 of their papers we have counts for

collaborators

9 papers

cs.LG2022

Semi-supervised Drifted Stream Learning with Short Lookback

Weijieying Ren, Pengyang Wang, Xiaolin Li +2

In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not alway…

cs.AI2022

Reinforced Imitative Graph Learning for Mobile User Profiling

Dongjie Wang, Pengyang Wang, Yanjie Fu +3

Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generat…

cs.CL20223 cited

Who Should Review Your Proposal? Interdisciplinary Topic Path Detection for Research Proposals

Meng Xiao, Ziyue Qiao, Yanjie Fu +5

The peer merit review of research proposals has been the major mechanism to decide grant awards. Nowadays, research proposals have become increasingly interdisciplinary. It has bee…

cs.LG2021

Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning

Dongjie Wang, Kunpeng Liu, David Mohaisen +3

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, Spatial Representation Learning (SRL) use…

cs.AI20212 cited

Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective

Dongjie Wang, Pengyang Wang, Kunpeng Liu +3

In this paper, we study the problem of mobile user profiling, which is a critical component for quantifying users' characteristics in the human mobility modeling pipeline. Human mo…

cs.SI20204 cited

Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning

Ziyue Qiao, Pengyang Wang, Yanjie Fu +3

While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem.…