activity
20242026
most citedNetwork Learning with Directional Sign Patterns

1 citations · 1 across the 8 of their papers we have counts for

collaborators

8 papers

eess.SY2026

Maximum-Entropy Random Walks on Hypergraphs

Anqi Dong, Anzhi Sheng, Xin Mao +1

Random walks are fundamental tools for analyzing complex networked systems, including social networks, biological systems, and communication infrastructures. While classical random…

eess.SY2025

Data-driven model order reduction for T-Product-Based dynamical systems

Shenghan Mei, Ziqin He, Yidan Mei +4

Model order reduction plays a crucial role in simplifying complex systems while preserving their essential dynamic characteristics, making it an invaluable tool in a wide range of…

math.DS2025

Tensor-based homogeneous polynomial dynamical system analysis from data

Xin Mao, Anqi Dong, Ziqin He +3

Numerous complex real-world systems, such as those in biological, ecological, and social networks, exhibit higher-order interactions that are often modeled using polynomial dynamic…

eess.SY2024

Data-driven Analysis of T-Product-based Dynamical Systems

Xin Mao, Anqi Dong, Ziqin He +2

A wide variety of data can be represented using third-order tensors, spanning applications in chemometrics, psychometrics, and image processing. However, traditional data-driven fr…

eess.SY2024

Controllability and Observability of Temporal Hypergraphs

Anqi Dong, Xin Mao, Can Chen

Numerous complex systems, such as those arisen in ecological networks, genomic contact networks, and social networks, exhibit higher-order and time-varying characteristics, which c…

cs.CE2024

Heterogeneous Entity Representation for Medicinal Synergy Prediction

Jiawei Wu, Jun Wen, Mingyuan Yan +4

Medicinal synergy prediction is a powerful tool in drug discovery and development that harnesses the principles of combination therapy to enhance therapeutic outcomes by improving…

math.OC2024★ 1 cited

Network Learning with Directional Sign Patterns

Anqi Dong, Can Chen, Tryphon T. Georgiou

Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g…

eess.SY2024

On Complexity of Stability Analysis in Higher-order Ecological Networks through Tensor Decompositions

Anqi Dong, Can Chen

Complex ecological networks are often characterized by intricate interactions that extend beyond pairwise relationships. Understanding the stability of higher-order ecological netw…