Abstract
This research studies the problem of node ranking in a random network. Specifically, we consider a Markov chain with several ergodic classes and unknown transition probabilities which can be estimated by sampling. The objective is to select all of the best nodes in each ergodic class. A sampling procedure is proposed to decompose the Markov chain and maximize a weighted probability of correct selection of the best nodes in each ergodic class. Numerical results demonstrate the efficiency of the proposed sampling procedure.
| Original language | English |
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| Title of host publication | 2019 Winter Simulation Conference (WSC) |
| Subtitle of host publication | [Proceedings] |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 3752-3763 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781728132839 |
| ISBN (Print) | 9781728120522 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 2019 Winter Simulation Conference, WSC 2019 - National Harbor, United States Duration: 8 Dec 2019 → 11 Dec 2019 |
Publication series
| Name | Proceedings - Winter Simulation Conference |
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| Volume | 2019-December |
| ISSN (Print) | 0891-7736 |
Conference
| Conference | 2019 Winter Simulation Conference, WSC 2019 |
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| Country/Territory | United States |
| City | National Harbor |
| Period | 8/12/19 → 11/12/19 |
Funding
This work was supported in part by the National Science Foundation of China (NSFC) under Grants 71571048, 71720107003, 71690232, and 61603321, and by the National Science Foundation under Awards ECCS-1462409 and CMMI-1462787.
