32 citations · 58 across the 5 of their papers we have counts for
5 papers
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
Tailin Wu, Qinchen Wang, Yinan Zhang +7
Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications…
IA-GCN: Interactive Graph Convolutional Network for Recommendation
Yinan Zhang, Pei Wang, Congcong Liu +7
Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn inform…
Learning Hierarchical Review Graph Representations for Recommendation
Yong Liu, Susen Yang, Yinan Zhang +3
The user review data have been demonstrated to be effective in solving different recommendation problems. Previous review-based recommendation methods usually employ sophisticated…
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Yong Liu, Yinan Zhang, Qiong Wu +5
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years. Most previous…
Reductive Clustering: An Efficient Linear-time Graph-based Divisive Cluster Analysis Approach
Ching Tarn, Yinan Zhang, Ye Feng
We propose an efficient linear-time graph-based divisive cluster analysis approach called Reductive Clustering. The approach tries to reveal the hierarchical structural information…