Exploiting Reinforcement-Learning for Influence Maximization without Human-Annotated Data
碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 104 === Strategies to choose nodes on a social network to maximize the total influence has been studied for decades. Studies have shown that the greedy algorithm is a competitive strategy and it has been proved to cover at least 63% of the optimal spread. Here we pr...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2015
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| Online Access: | http://ndltd.ncl.edu.tw/handle/24425905249677578391 |