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恢复暂时重新连线的网络：基于模型的方法
Recovering Temporally Rewiring Networks: A model-based approach |
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| 课程网址: |
http://videolectures.net/icml07_guo_rtr/
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| 主讲教师: |
Fan Guo
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| 开课单位: |
卡内基梅隆大学
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| 开课时间: |
2007-06-23
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| 课程语种: |
英语
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| 中文简介: |
动态系统（例如活细胞或社交社区）中的实体之间的关系信息的合理表示是随机网络，其在拓扑上重新布线并且随着时间的推移在语义上演变。虽然有关于静态或时间不变网络建模的丰富文献，但对于重新布线网络下的动态过程建模以及在不可观察时恢复这些网络的工作要少得多。我们提出了一类隐藏的时间指数随机图模型（htERGM）来研究尚未开发的主题建模和从时间序列的节点属性恢复时间重新布线网络，如社会行动者的活动或基因的表达水平。我们表明，人们可以从观察中可靠地推断出演化网络的潜在时间特定拓扑。我们报告了合成数据和果蝇生命周期基因表达数据集的经验结果，与htERGM的静态对应物进行了比较。
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| 课程简介: |
A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring and semantically evolving over time. While there is a rich literature on modeling static or temporally invariant networks, much less has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. We present a class of hidden temporal exponential random graph models (htERGMs) to study the yet unexplored topic of modeling and recovering temporally rewiring networks from time series of node attributes such as activities of social actors or expression levels of genes. We show that one can reliably infer the latent timespecific topologies of the evolving networks from the observation. We report empirical results on both synthetic data and a Drosophila lifecycle gene expression data set, in comparison with a static counterpart of htERGM.
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| 关 键 词: |
动态系统; 随机网络; 网络建模
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-22：chenxin
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| 阅读次数: |
39
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