activity
20212023
most citedGraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

358 citations · 412 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20236 cited

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

Tianxiang Zhao, Wenchao Yu, Suhang Wang +6

Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations. However, exi…

cs.SI202229 cited

Exploring Edge Disentanglement for Node Classification

Tianxiang Zhao, Xiang Zhang, Suhang Wang

Edges in real-world graphs are typically formed by a variety of factors and carry diverse relation semantics. For example, connections in a social network could indicate friendship…

cs.LG20215 cited

Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network

Yuqing Hu, Xiaoyuan Cheng, Suhang Wang +3

The world is increasingly urbanizing and the building industry accounts for more than 40% of energy consumption in the United States. To improve urban sustainability, many cities a…

cs.LG202114 cited

Semi-Supervised Graph-to-Graph Translation

Tianxiang Zhao, Xianfeng Tang, Xiang Zhang +1

Graph translation is very promising research direction and has a wide range of potential real-world applications. Graph is a natural structure for representing relationship and int…

cs.LG2021358 cited

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

Tianxiang Zhao, Xiang Zhang, Suhang Wang

Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. However, exist…