图 书 馆
提取社会事件以学习更好的信息扩散模型Extracting Social Events for Learning Better Information Diffusion Models |
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| 课程网址: | http://videolectures.net/kdd2013_lin_diffusion_models/ |
| 主讲教师: | Shuyang Lin |
| 开课单位: | 伊利诺大学 |
| 开课时间: | 2013-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  信息传播模型的学习是社会网络中信息传播研究的一个基本问题。现有方法从社交网络中的事件中学习扩散模型。但是，社交网络中的事件可能具有不同的根本原因。其中一些可能是由网络内部的社会影响引起的，而另一些则可能反映了“现实世界”中的外部趋势。关于扩散模型学习的大多数现有工作都没有将社会影响引起的事件与外部趋势引起的事件区分开。 本文中，我们从社交网络的数据流中提取社会事件，然后使用提取的社交事件来改善信息传播模型的学习。我们提出了一个LADP（潜在行动扩散路径）模型，将信息扩散模型与外部趋势模型相结合，然后设计一种基于EM的算法来有效地推断出扩散概率，外部趋势和事件的来源。 |
| 课程简介: | Learning of the information diffusion model is a fundamental problem in the study of information diffusion in social networks. Existing approaches learn the diffusion models from events in social networks. However, events in social networks may have different underlying reasons. Some of them may be caused by the social influence inside the network, while others may reflect external trends in the "real world". Most existing work on the learning of diffusion models does not distinguish the events caused by the social influence from those caused by external trends. In this paper, we extract social events from data streams in social networks, and then use the extracted social events to improve the learning of information diffusion models. We propose a LADP (Latent Action Diffusion Path) model to incorporate the information diffusion model with the model of external trends, and then design an EM-based algorithm to infer the diffusion probabilities, the external trends and the sources of events efficiently. |
| 关 键 词: | 模型学习; 信息传播 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-10：zyk |
| 最后编审: | 2020-11-10：zyk |
| 阅读次数: | 31 |