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Demo Data from EDBT 2026 / reviewers in the wild / expert

Qingqian Tu

dblp:432/6660 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0006-7155-3149ORCID · 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
Multi-agent systems · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › agent-based simulation › social simulation
agent-based social simulation		 1.012026
PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization · WWW 2026

Methods — techniques the papers use, named apart from their topics

large language model agents · 2.0
YearPublicationVenuePosition
2026 PolicySim: An LLM-Based Agent Social Simulation Sandbox for Proactive Policy Optimization
Renhong Huang, Jiarong Xu, Qingqian Tu, Sheng Guo 0005, Bo Zheng 0007, Yang Yang 0009
 WWW5