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利用网络特性模拟社交网络中的扩散

Modeling Diffusion in Social Networks Using Network Properties
课程网址: http://videolectures.net/icwsm2012_luu_network/  
主讲教师: Minh-Duc Luu
开课单位: 新加坡管理大学
开课时间: 2012-07-06
课程语种: 英语
中文简介:
由于物品通过口口传播和外部因素传播,物品的扩散发生在社交网络中。这些项目可能是新闻,产品,视频,广告或传染病毒。以前的研究已经研究了宏观和微观层面的扩散过程。前者模拟了扩散过程中项目采用者的数量,而后者决定了哪些个体采用项目。在本文中,我们建立了一个通用的概率框架,可用于推导宏观水平扩散模型,包括众所周知的低音模型(BM)。使用这个框架,我们开发了几个其他模型,考虑到社交网络的度分布以及相邻采用者在扩散过程中线性影响的假设。通过对合成数据的一些评估,本文表明在扩散过程中度分布实际上发生了变化。因此,我们引入了多阶段扩散模型来应对可变度分布。通过对合成和真实数据集进行实验,我们证明了我们提出的扩散模型可以从观察到的扩散数据中恢复扩散参数,这使我们能够以高精度模拟扩散。
课程简介: Diffusion of items occurs in social networks due to spreading of items through word of mouth and exogenous factors. These items may be news, products, videos, advertisements or contagious viruses. Previous research has studied diffusion process at both the macro and micro levels. The former models the number of item adopters in the diffusion process while the latter determines which individuals adopt item. In this paper, we establish a general probabilistic framework, which can be used to derive macro-level diffusion models, including the well known Bass Model (BM). Using this framework, we develop several other models considering the social network’s degree distribution coupled with the assumption of linear influence by neighboring adopters in the diffusion process. Through some evaluation on synthetic data, this paper shows that degree distribution actually changes during the diffusion process. We therefore introduce a multi-stage diffusion model to cope with variable degree distribution. By conducting experiments on both synthetic and real datasets, we show that our proposed diffusion models can recover the diffusion parameters from the observed diffusion data, which allows us to model diffusion with high accuracy.
关 键 词: 社交网络; 概率框架; 低音模型
课程来源: 视频讲座网
最后编审: 2019-04-27:lxf
阅读次数: 69