Demo Data from EDBT 2026 / reviewers in the wild / expert
Wencai Cao
dblp:372/2028
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0008-7796-8227ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Graph learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Graph learning
graph neural network |
0.8 | 1 | 2024 | Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach · WWW 2024 |
Machine learning › Graph learning › graph signal processing › graph filter
polynomial graph filter |
0.8 | 1 | 2024 | Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach · WWW 2024 |
Machine learning › Graph learning › graph signal processing
spectral graph filter |
0.8 | 1 | 2024 | Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach · WWW 2024 |
Methods — techniques the papers use, named apart from their topics
polynomial approximation · 0.8krylov subspace · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace ApproachabstractGraph Neural Networks (GNNs), known as spectral graph filters, find a wide range of applications in web networks. To bypass eigendecomposition, polynomial graph filters are proposed to approximate graph filters by leveraging various polynomial bases for filter training. However, no existing studies have explored the diverse polynomial graph filters from a unified perspective for optimization. Keke Huang, Wencai Cao, Hoang Ta 0001, Xiaokui Xiao, Pietro Liò |
WWW | 2 |