Research map / Learning theory and algorithms / Clustering
Clustering via Hedonic Games: New Concepts and Algorithms
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Connects graphical hedonic games (friendship and enemy graphs) with clustering, framing data-point similarity as friendship and dissimilarity as enmity to study clustering concepts and algorithms.
Problem
Understanding the connection between coalition formation games and clustering, where clusters correspond to stable coalitions under compact preference representations of friendship and enmity.
Approach
Models clustering as graphical hedonic games: friendship relations align with data point similarities and enmity with dissimilarities, bringing hedonic game stability concepts into clustering.
Closest papers by meaning
- Complexity of Probabilistic Inference in Random Dichotomous Hedonic Games (AAAI 2023 · similarity 0.89)
- Forming Probably Stable Communities with Limited Interactions (AAAI 2019 · similarity 0.89)
- Graphical Hedonic Games of Bounded Treewidth (AAAI 2016 · similarity 0.89)
- Stability in Online Coalition Formation (AAAI 2024 · similarity 0.88)
- Individually Stable Dynamics in Coalition Formation over Graphs (AAAI 2025 · similarity 0.87)
- Learning Coalition Structures with Games (AAAI 2024 · similarity 0.86)
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