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随机图模型的信息理论比较：一些实验Information Theoretic Comparison of Stochastic Graph Models: Some Experiments |
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| 课程网址: | http://videolectures.net/waw09_lang_itesgm/ |
| 主讲教师: | Kevin J. Lang |
| 开课单位: | 雅虎公司 |
| 开课时间: | 2009-03-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The Modularity-Q measure of community structure is known to falsely ascribe community structure to random graphs, at least when it is naively applied. Although Q is motivated by a simple kind of comparison of stochastic graph models, it has been suggested that a more careful comparison in an information-theoretic framework might avoid problems like this one. Most earlier papers exploring this idea have ignored the issue of skewed degree distributions and have only done experiments on a few small graphs. By means of a large-scale experiment on over 100 large complex networks, we have found that modeling the degree distribution is essential. Once this is done, the resulting information-theoretic clustering measure does indeed avoid Q’s bad property of seeing cluster structure in random graphs. |
| 关 键 词: | 建模; 聚类; 群落结构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-04：zyk |
| 阅读次数: | 29 |