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 observations. We formulate SFL as a statistical learning problem in which optimal subgraph operators are inherently data-dependent. To address the difficulty of directly estimating such operators, we develop a subgraph filter algebra based on distance-aware Laplacian constructions, def
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Filter Learning for Subgraphs: Algebras and Performance Risk” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Purui Zhang →
“Filter Learning for Subgraphs: Algebras and Performance Risk Bounds”
- LinkedLinked via arxiv author · 85%Xuefeng Jin →
“Filter Learning for Subgraphs: Algebras and Performance Risk Bounds”
- LinkedLinked via arxiv author · 85%Yanan Zhao →
“Filter Learning for Subgraphs: Algebras and Performance Risk Bounds”
- LinkedLinked via arxiv author · 85%Bihan Wen →
“Filter Learning for Subgraphs: Algebras and Performance Risk Bounds”
- LinkedLinked via arxiv author · 85%Wee Peng Tay →
“Filter Learning for Subgraphs: Algebras and Performance Risk Bounds”