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重叠社区的可扩展推理Scalable Inference of Overlapping Communities |
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| 课程网址: | http://videolectures.net/machine_gopalan_overlapping/ |
| 主讲教师: | Prem Gopalan |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 2013-06-11 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel. It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60,000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of communities in large real-world networks, and detects the true communities in 280 benchmark networks with equal or better accuracy compared to other scalable algorithms. |
| 关 键 词: | 可扩展; 算法; 重叠社区 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cwx |
| 阅读次数: | 59 |