activity
20192022
most citedAn Attention-based Graph Neural Network for Heterogeneous Structural Learning

24 citations · 28 across the 3 of their papers we have counts for

collaborators

5 papers

cs.RO2022

Relative velocity-based reward functions for crowd navigation of robots

Xiaoqing Yang, Fei Li

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tas…

cs.LG2020

Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution

Yucheng Lin, Huiting Hong, Xiaoqing Yang +3

Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/e…

cs.LG201924 cited

An Attention-based Graph Neural Network for Heterogeneous Structural Learning

Huiting Hong, Hantao Guo, Yucheng Lin +3

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.…

cs.CL20194 cited

Interpretable Text Classification Using CNN and Max-pooling

Hao Cheng, Xiaoqing Yang, Zang Li +2

Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the i…

cs.SI2019

AHINE: Adaptive Heterogeneous Information Network Embedding

Yucheng Lin, Xiaoqing Yang, Zang Li +1

Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensiona…