5 papers
Multi-Source Wasserstein Distributionally Robust Graph Learning
Chuansen Peng, Yifan Xia, Jinshan Zhong +1
Reconstructing complex network topologies from data is a fundamental challenge in cybernetics and graph signal processing, with applications in neuroscience, sensor, and social net…
Koopman Lifting with Certified Error Bounds for Joint Inference in Nonlinear Networks
Chuansen Peng, Xiaojing Shen, Yunmin Zhu
Jointly inferring latent node states and unknown network topology in nonlinear graphical dynamical systems is a fundamental yet largely unsolved problem, where the mutual entanglem…
Dynamic Elliptical Graph Factor Models via Riemannian Optimization with Geodesic Temporal Regularization
Chuansen Peng, Xiaojing Shen
Inferring time-varying graph structures from high-dimensional nodal observations is a fundamental problem arising in neuroscience, finance, climatology, and beyond. Two intrinsic c…
Learning Time-Varying Graphs from Incomplete Graph Signals
Chuansen Peng, Xiaojing Shen
This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unifie…
Network Topology Inference from Smooth Signals Under Partial Observability
Chuansen Peng, Hanning Tang, Zhiguo Wang +1
Inferring network topology from smooth signals is a significant problem in data science and engineering. A common challenge in real-world scenarios is the availability of only part…