The stable marriage problem: structure and algorithms
The stable marriage problem: structure and algorithms
On-line algorithms for weighted bipartite matching and stable marriages
Theoretical Computer Science
A sublinear parallel algorithm for stable matching
Theoretical Computer Science
A fixed-point approach to stable matchings and some applications
Mathematics of Operations Research
Social Networks and Stable Matchings in the Job Market
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Almost Stable Matchings by Truncating the Gale–Shapley Algorithm
Algorithmica - Special Issue: Matching Under Preferences; Guest Editors: David F. Manlove, Robert W. Irving and Kazuo Iwama
Contribution games in social networks
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Autonomous actor positioning in wireless sensor and actor networks using stable-matching
International Journal of Parallel, Emergent and Distributed Systems - Best papers from the WWASN2009 workshop
Uncoordinated Two-Sided Matching Markets
SIAM Journal on Computing
Market sharing games applied to content distribution in ad hoc networks
IEEE Journal on Selected Areas in Communications
Locally stable marriage with strict preferences
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II
Socially stable matchings in the hospitals/residents problem
WADS'13 Proceedings of the 13th international conference on Algorithms and Data Structures
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We study stable marriage and roommates problems under locality constraints. Each player is a vertex in a social network and strives to be matched to other players. The value of a match is specified by an edge weight. Players explore possible matches only based on their current neighborhood. We study convergence of natural better-response dynamics that converge to locally stable matchings - matchings that allow no incentive to deviate with respect to their imposed information structure in the social network. If we have global information and control to steer the convergence process, then quick convergence is possible and for every starting state we can construct in polynomial time a sequence of polynomially many better-response moves to a locally stable matching. In contrast, for a large class of oblivious dynamics including random and concurrent better-response the convergence time turns out to be exponential. In such distributed settings, a small amount of random memory can ensure polynomial convergence time, even for many-to-many matchings and more general notions of neighborhood. Here the type of memory is crucial as for several variants of cache memory we provide exponential lower bounds on convergence times.