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社会网络中的序贯影响模型
Sequential Influence Models in Social Networks |
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| 课程网址: |
http://videolectures.net/icwsm2010_suri_sim/
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| 主讲教师: |
Siddharth Suri
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| 开课单位: |
雅虎公司
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| 开课时间: |
信息不详。欢迎您在右侧留言补充。
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| 课程语种: |
英语
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| 中文简介: |
在一个社会网络中，个人之间的影响传播可以自然地在概率框架中进行建模，但要解释各种模型之间的差异以及将这些模型与实际的社会网络数据联系起来是很有挑战性的。在这里，我们考虑两个最基本的影响定义，一个基于社会网络的一小部分“快照”观测，另一个基于详细的时间动态。前者特别有用，因为大型社交网络数据集通常只在快照或爬行中可用。然而，后者提供了一个更详细的过程模型，说明影响是如何传播的。我们研究了这两种测量影响的方法之间的关系，特别是建立了如何从更容易观察到的快照测量中推断出更详细的时间测量。我们使用维基百科上的社会互动历史来验证我们的分析；结果是第一个大规模的研究，展示了社会影响的快照和时间模型之间的直接关系。
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| 课程简介: |
The spread of influence among individuals in a social network can be naturally modeled in a probabilistic framework, but it is challenging to reason about differences between various models as well as to relate these models to actual social network data. Here we consider two of the most fundamental definitions of influence, one based on a small set of "snapshot'' observations of a social network and the other based on detailed temporal dynamics. The former is particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however provides a more detailed process model of how influence spreads. We study the relationship between these two ways of measuring influence, in particular establishing how to infer the more detailed temporal measure from the more readily observable snapshot measure. We validate our analysis using the history of social interactions on Wikipedia; the result is the first large-scale study to exhibit a direct relationship between snapshot and temporal models of social influence.
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| 关 键 词: |
概率框架; 时间尺度; 社会网络数据集
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-01-13：chenxin
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| 阅读次数: |
31
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