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学习混合隶属度社区模型的张量谱方法A Tensor Spectral Approach to Learning Mixed Membership Community Models |
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| 课程网址: | http://videolectures.net/colt2013_ge_models/ |
| 主讲教师: | Rong Ge |
| 开课单位: | 普林斯顿大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Modeling community formation and detecting hidden communities in networks is a well studied problem. However, theoretical analysis of community detection has been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we remove this restriction, and consider a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced in Aioroldi et. al (2008). This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. We propose a unified approach to learning these models via a tensor spectral decomposition method. Our estimator is based on low-order moment tensor of the observed network, consisting of 3-star counts. Our learning method is fast and is based on simple linear algebra operations, e.g. singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters and present a careful finite sample analysis of our learning method. Additionally, our results match the best known scaling requirements in the special case of the stochastic block model. |
| 关 键 词: | 计算机科学; 机器学习; 随机网络模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-19：cwx |
| 阅读次数: | 40 |