most citedSymbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction

13 citations · 15 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SI2020

Decoupled Variational Embedding for Signed Directed Networks

Xu Chen, Jiangchao Yao, Maosen Li +2

Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and…

cs.IR2020

Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

Jingchao Su, Xu Chen, Ya Zhang +3

Relational learning aims to make relation inference by exploiting the correlations among different types of entities. Exploring relational learning on multiple bipartite graphs has…

cs.IR2020

Dual Graph Embedding for Object-Tag LinkPrediction on the Knowledge Graph

Chenyang Li, Xu Chen, Ya Zhang +3

Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates…

cs.IR2019

Cascading: Association Augmented Sequential Recommendation

Xu Chen, Kenan Cui, Ya Zhang +1

Recently, recommendation according to sequential user behaviors has shown promising results in many application scenarios. Generally speaking, real-world sequential user behaviors…

cs.CV201913 cited

Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction

Maosen Li, Siheng Chen, Xu Chen +3

3D skeleton-based action recognition and motion prediction are two essential problems of human activity understanding. In many previous works: 1) they studied two tasks separately,…

stat.ML2019

Node Attribute Generation on Graphs

Xu Chen, Siheng Chen, Huangjie Zheng +4

Graph structured data provide two-fold information: graph structures and node attributes. Numerous graph-based algorithms rely on both information to achieve success in supervised…