EDBT 2026 Demo / reviewers in the wild / expert
Yongyi Lin
dblp:232/5047
· 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
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Theoretical computer science
1 paper |
Algorithmic game theory and mechanism design · 100% | |
| Artificial intelligence
1 paper |
Multi-agent systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities
opinion dynamics |
1.0 | 1 | 2026 | Game Theory Based Community-Aware Opinion Dynamics · AAAI 2026 |
Algorithmic game theory and mechanism design
network games |
1.0 | 1 | 2026 | Game Theory Based Community-Aware Opinion Dynamics · AAAI 2026 |
Knowledge, reasoning and agents › Multi-agent systems › agent-based simulation › social simulation
agent-based social simulation |
0.3 | 1 | 2026 | Game Theory Based Community-Aware Opinion Dynamics · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
non-cooperative game · 3.0co-evolutionary framework · 3.0bounded-confidence model · 3.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Game Theory Based Community-Aware Opinion DynamicsabstractUnderstanding opinion evolution in complex social networks is crucial for modeling social influence and predicting collective behavior. Yet, most models overlook how community structures shape opinion updates, often assuming homogeneous influence. This abstraction neglects individuals’ stronger responsiveness to intra-community peers—an empirically observed driver of localized consensus and inter-group polarization. We propose GCAOFP, a co-evolutionary framework that jointly models opinion dynamics and community formation as an integrated process. In GCAOFP, agents strategically alternate between two coupled modules: (1) a Community Dynamics Module, where agents play a non-cooperative game to optimize community memberships based on opinion alignment and structural cohesion; and (2) an Opinion Adjustment Module, where agents revise opinions via a bounded-confidence mechanism modulated by community-induced influence weights. This dual-stage process captures the feedback loop between structure and opinion. We prove that GCAOFP converges to stable equilibria, ensuring intra-community consensus and inter-community diversity—dynamics that standard models fail to replicate. Experiments on real-world networks show that GCAOFP better reproduces localized opinion clusters, while offering strong scalability and interpretability, illuminating the strategic foundations of polarization. Shanfan Zhang, Yongyi Lin, Xiaoting Shen, Zhan Bu |
AAAI | 2 |