4 citations · 5 across the 6 of their papers we have counts for
6 papers
Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting
Mengzhou Gao, Huangqian Yu, Pengfei Jiao
Time series in real-world applications are often generated by nonlinear dynamical systems, making accurate forecasting challenging. Existing approaches that explicitly model system…
One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
Mengzhou Gao, Kaiwei Wang, Pengfei Jiao
Neural Flows efficiently model irregular multivariate time series by directly learning ODE solution trajectories with neural networks, bypassing step-by-step numerical solvers. Des…
Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning
Xinxun Zhang, Pengfei Jiao, Mengzhou Gao +2
Although dynamic graph neural networks (DyGNNs) have demonstrated promising capabilities, most existing methods ignore out-of-distribution (OOD) shifts that commonly exist in dynam…
Heterogeneous Temporal Hypergraph Neural Network
Huan Liu, Pengfei Jiao, Mengzhou Gao +2
Graph representation learning (GRL) has emerged as an effective technique for modeling graph-structured data. When modeling heterogeneity and dynamics in real-world complex network…
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality
Zehao Liu, Mengzhou Gao, Pengfei Jiao
Multivariate time series anomaly detection has numerous real-world applications and is being extensively studied. Modeling pairwise correlations between variables is crucial. Exist…
Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs
Pengfe Jiao, Xinxun Zhang, Mengzhou Gao +2
Generative self-supervised learning (SSL), especially masked autoencoders (MAE), has greatly succeeded and garnered substantial research interest in graph machine learning. However…