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推断扩散和影响网络

Inferring Networks of Diffusion and Influence
课程网址: http://videolectures.net/kdd2010_gomez_ind/  
主讲教师: Manuel Gomez Rodriguez
开课单位: 斯坦福大学
开课时间: 2010-10-01
课程语种: 英语
中文简介:
信息传播和病毒传播是网络中谈论的基本过程。虽然通常可以直接观察节点何时被感染,但观察个体传播(即感染谁或谁影响谁)通常是非常困难的。此外,在许多应用中,扩散和传播扩散的基础网络实际上是未被观察到的。我们通过开发一种通过网络追踪扩散和影响路径并推断传染传播的网络来解决这些挑战。鉴于节点采用信息或被感染的时间,我们确定最佳解释观察到的感染时间的最佳网络。由于优化问题是NP难以精确解决,我们开发了一种有效的近似算法,可以扩展到大型数据集,并且在实践中可以证明接近最优的性能。我们通过在一年的时间内在一组1.7亿个博客和新闻文章中跟踪信息级联来推断我们的方法的有效性,以推断信息如何流经在线媒体空间。我们发现新闻的传播网络往往具有核心外围结构,其中有一小组核心媒体网站将信息传播到网络的其他部分。这些网站往往具有稳定的影响圈,更多的一般新闻媒体网站充当它们之间的连接器。
课程简介: Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.
关 键 词: 信息传播; 病毒传播; 网络追踪扩散
课程来源: 视频讲座网
入库时间: 2019-05-11
最后编审: 2019-05-11:lxf
阅读次数: 19