Better Community Detection given Link Prediction - A Joint Optimization Framework
碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 105 === Real world network data can be incomplete due to reasons such as data subsampling, privacy protection, etc. Consequently, communities identified based on such incomplete network information could be not as reliable as the ones identified based on the fully o...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/v3ym7q |
| Summary: | 碩士 === 國立臺灣大學 === 資訊網路與多媒體研究所 === 105 === Real world network data can be incomplete due to reasons such as data subsampling, privacy protection, etc. Consequently, communities identified based on such incomplete network information could be not as reliable as the ones identified based on the fully observed information. In this paper, a joint optimization framework COPE is proposed to improve community detection quality through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Through the experiments, we have observed that our joint framework outperforms the interactive 2-stage approach as well as several state-of-the-art community detection algorithms.
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