Demo Data from SIGIR 2026 / reviewers in the wild / expert
Chuangyi Li
dblp:422/2463
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 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.
| Computer networks
1 paper |
Edge and fog computing · 50% Network optimization and economics · 50% | |
| Artificial intelligence
1 paper |
Efficient and distributed learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network optimization and economics › resource allocation
cooperative scheduling |
1.0 | 1 | 2026 | Federated MADDPG-Based Collaborative Scheduling Strategy in Vehicular Edge Computing · IEEE Trans. Mob. Comput. 2026 |
Edge and fog computing › mobile edge computing
vehicular edge computing |
1.0 | 1 | 2026 | Federated MADDPG-Based Collaborative Scheduling Strategy in Vehicular Edge Computing · IEEE Trans. Mob. Comput. 2026 |
Machine learning › Efficient and distributed learning
federated learning |
0.3 | 1 | 2026 | Federated MADDPG-Based Collaborative Scheduling Strategy in Vehicular Edge Computing · IEEE Trans. Mob. Comput. 2026 |
Methods — techniques the papers use, named apart from their topics
multi-agent deep deterministic policy gradient · 2.0federated learning · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Federated MADDPG-Based Collaborative Scheduling Strategy in Vehicular Edge Computing
Songxin Lei, Huijun Tang, Chuangyi Li, Chenli Xu, Huaming Wu |
IEEE Trans. Mob. Comput. | 3 |