11 papers
Filter Learning for Subgraphs: Algebras and Performance Risk Bounds
Purui Zhang, Feng Ji, Yanan Zhao +2
Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a system…
Graph Distribution-valued Signals in Wasserstein Spaces: Theory and Applications
Yanan Zhao, Feng Ji, Xingchao Jian +1
We introduce a framework for graph signal processing (GSP) in which signals are represented as graph distribution-valued signals (GDSs), i.e., probability measures in a Wasserstein…
Optimal Sensor Placement via Graph-constrained Flow Matching
Feng Ji, Jingyang Dai, Wee Peng Tay +1
Optimal sensor placement is a fundamental problem in graph signal processing (GSP), where a limited number of sensors are deployed to reconstruct a continuous signal field. Existin…
Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
Yanan Zhao, Feng Ji, Jingyang Dai +4
Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods typically rely on fixed, handcrafted views-…
Graph Distribution-valued Signals: A Wasserstein Space Perspective
Yanan Zhao, Feng Ji, Xingchao Jian +1
We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs), which are probability distributions in the Wassers…
Uncertainty Principle for Vertex-Time Graph Signal Processing
Yanan Zhao, Xingchao Jian, Feng Ji +2
We present an uncertainty principle for graph signals in the vertex-time domain, unifying the classical time-frequency and graph uncertainty principles within a single framework. B…