activity
20202023
most citedCoSimGNN: Towards Large-scale Graph Similarity Computation

4 citations · 9 across the 7 of their papers we have counts for

collaborators

7 papers

cs.HC2023★ 2 cited

The COMMOTIONS Urban Interactions Driving Simulator Study Dataset

Aravinda Ramakrishnan Srinivasan, Julian Schumann, Yueyang Wang +7

Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is…

cs.LG2023

Modeling human road crossing decisions as reward maximization with visual perception limitations

Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Jussi P. P. Jokinen +2

Understanding the interaction between different road users is critical for road safety and automated vehicles (AVs). Existing mathematical models on this topic have been proposed b…

cs.SI2021★ 1 cited

Connecting Latent ReLationships over Heterogeneous Attributed Network for Recommendation

Ziheng Duan, Yueyang Wang, Weihao Ye +3

Recently, deep neural network models for graph-structured data have been demonstrating to be influential in recommendation systems. Graph Neural Network (GNN), which can generate h…

cs.LG2020★ 1 cited

Multivariate Time Series Classification with Hierarchical Variational Graph Pooling

Ziheng Duan, Haoyan Xu, Yueyang Wang +5

With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC technique…

cs.LG2020

MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting

Yueyang Wang, Ziheng Duan, Yida Huang +3

Multivariate time series forecasting, which analyzes historical time series to predict future trends, can effectively help decision-making. Complex relations among variables in MTS…

cs.LG2020★ 1 cited

Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation

Haoyan Xu, Ziheng Duan, Jie Feng +4

Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compound…

cs.LG2020★ 4 cited

CoSimGNN: Towards Large-scale Graph Similarity Computation

Haoyan Xu, Runjian Chen, Yueyang Wang +2

The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values…