图 书 馆
论潜在社会网络推理的凸性On the Convexity of Latent Social Network Inference |
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| 课程网址: | http://videolectures.net/nips2010_myers_cls/ |
| 主讲教师: | Seth A. Myers |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2011-01-12 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In many real-world scenarios, it is nearly impossible to collect explicit social network data. In such cases, whole networks must be inferred from underlying observations. Here, we formulate the problem of inferring latent social networks based on network diffusion or disease propagation data. We consider contagions propagating over the edges of an unobserved social network, where we only observe the times when nodes became infected, but not who infected them. Given such node infection times, we then identify the optimal network that best explains the observed data. We present a maximum likelihood approach based on convex programming with a1-like penalty term that encourages sparsity. Experiments on real and synthetic data reveal that our method near-perfectly recovers the underlying network structure as well as the parameters of the contagion propagation model. Moreover, our approach scales well as it can infer optimal networks on thousands of nodes in a matter of minutes. |
| 关 键 词: | 社交网络数据; 基础观察; 边缘传播 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-25：cwx |
| 阅读次数: | 57 |