The end-to-end effects of Internet path selection
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Near-optimal network design with selfish agents
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Proceedings of the twenty-second annual symposium on Principles of distributed computing
On nash equilibria for a network creation game
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
On the topologies formed by selfish peers
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Recent game-theoretic approaches to constructing overlay network topologies have not been scalable. This paper introduces a machine learning approach to constructing overlay networks. The machine learning approach learns characteristics from small networks constructed using a game-theoretic approach. The knowledge learned is then used to construct larger networks. The results show that the machine learning approach closely approximates the game-theoretic networks for a wide range of network parameters, while being scalable.