图 书 馆
群体规模的信息级联Information Cascade at Group Scale |
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| 课程网址: | http://videolectures.net/kdd2013_eftekhar_information_cascade/ |
| 主讲教师: | Milad Eftekhar |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2013-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  在社交网络中确定k个最具影响力的人是一个经过充分研究的问题。目的是检测（社会）网络中的k个人，如果他们被说服采用新策略（产品，构想等），他们将影响最大人数。但是，在现实生活中，有些情况下，我们旨在激发群体而不是个人来触发网络扩散。诸如此类的情况比比皆是，例如广告牌，电视广告和报纸广告被广泛利用以提高受欢迎程度并提高知名度。 在本文中，我们概括了“影响节点”问题。也就是说，我们有兴趣找到网络中最“有影响力的群体”。作为解决此问题的第一篇论文：我们（1）针对基于组的问题提出了一种精细的信息扩散模型，（2）表明该过程是亚模块的，并提出了一种确定该模型下有影响力的组的算法（包括（3）提出了一个粗粒度模型，该模型在组级别（而非个人）检查网络，从而大大加快了大型网络的计算速度；（4）表明，我们在此处设计的扩散函数在一般情况下是亚模的，并针对该粗粒度模型提出了一种近似算法，最后通过在真实数据集上进行实验，（5）证明，与第一批采用者相比，所选群体的种子成员可以扩大传播范围（与有影响力的个人案例相比）。而且，我们可以更快地识别出这些有影响力的群体（高达1200万倍的提升速度），从而为该问题提供了切实可行的解决方案。 |
| 课程简介: | Identifying the k most influential individuals in a social network is a well-studied problem. The objective is to detect k individuals in a (social) network who will influence the maximum number of people, if they are independently convinced of adopting a new strategy (product, idea, etc). There are cases in real life, however, where we aim to instigate groups instead of individuals to trigger network diffusion. Such cases abound, e.g., billboards, TV commercials and newspaper ads are utilized extensively to boost the popularity and raise awareness. In this paper, we generalize the "influential nodes" problem. Namely we are interested to locate the most "influential groups" in a network. As the first paper to address this problem: we (1) propose a fine-grained model of information diffusion for the group-based problem, (2) show that the process is submodular and present an algorithm to determine the influential groups under this model (with a precise approximation bound), (3) propose a coarse-grained model that inspects the network at group level (not individuals) significantly speeding up calculations for large networks, (4) show that the diffusion function we design here is submodular in general case, and propose an approximation algorithm for this coarse-grained model, and finally by conducting experiments on real datasets, (5) demonstrate that seeding members of selected groups to be the first adopters can broaden diffusion (when compared to the influential individuals case). Moreover, we can identify these influential groups much faster (up to 12 million times speedup), delivering a practical solution to this problem. |
| 关 键 词: | 信息扩散; 近似算法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-29：zyk |
| 最后编审: | 2020-12-29：zyk |
| 阅读次数: | 30 |