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网络中具有成本效益的爆发检测

Cost-effective Outbreak Detection in Networks
课程网址: http://videolectures.net/solomon_leskovec_ceod/  
主讲教师: Jure Leskovec
开课单位: 斯坦福大学
开课时间: 2007-10-24
课程语种: 英语
中文简介:
我们应该阅读哪些博客以避免丢失重要信息?我们应该在水分配网络中的何处放置传感器以快速检测污染物?这些看似不同的问题具有相同的结构:可以将爆发检测建模为选择网络中的节点(博客,传感器位置等)的问题,以便尽快检测病毒或信息的传播。我们提出了在这些及相关问题中接近最佳传感器放置的通用方法。我们证明了许多现实的爆发检测目标(例如,发现可能性,受影响的人群)都具有“亚模块化”的特性。我们利用亚模量来开发一种有效的算法,该算法可扩展到大问题,可证明地实现了接近最佳的位置,同时比简单的贪婪算法快700倍。我们对一些现实世界中的大问题进行评估,包括水分配网络模型和真实博客数据。我们还将展示该方法如何在两个应用程序中带来更深刻的见解,解答多标准权衡,成本敏感性和泛化问题。
课程简介: Which blogs should we read to avoid missing important information? Where should we place sensors in a water distribution network to quickly detect contaminants? These seemingly different problems share common structure: Outbreak detection can be modeled as a problem of selecting nodes (blogs, sensor locations, ...) in a network, in order to detect the spreading of a virus or information as quickly as possible. We present a general methodology for near optimal sensor placement in these and related problems. We demonstrate that many realistic outbreak detection objectives (e.g., detection likelihood, population affected) exhibit the property of “submodularity’’. We exploit submodularity to develop an efficient algorithm that scales to large problems, provably achieving near optimal placements, while being 700 times faster than a simple greedy algorithm. We evaluate our approach on several large real-world problems, including a model of a water distribution network, and real blog data. We also show how the approach leads to deeper insights in both applications, answering multicriteria trade-off, cost-sensitivity and generalization questions.
关 键 词: 博客; 重要信息; 传感器
课程来源: 视频讲座网
最后编审: 2020-05-31:王勇彬(课程编辑志愿者)
阅读次数: 95