Author

Feng Ji

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Jul 2026

Filter Learning for Subgraphs: Algebras and Performance Risk Bounds

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 systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate ambient graph filters under partial observ...

Purui Zhang, Feng Ji, Yanan Zhao et al. · arXiv.org · 0 citations

Preprint Jul 2026

Graph Distribution-valued Signals in Wasserstein Spaces: Theory and Applications

By modeling the graph structure as a distribution conditioned on signal realizations, this framework provides a principled approach to signal-dependent graph structures, which are common in real-world applications, while explicitly encoding uncertainty in graph topology.

Yanan Zhao, Feng Ji, Xingchao Jian et al. · 0 citations