图 书 馆
融合：大型社交网络中的整合影响Confluence: Conformity Influence in Large Social Networks |
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| 课程网址: | http://videolectures.net/kdd2013_tang_conformity_influence/ |
| 主讲教师: | Jie Tang |
| 开课单位: | 清华大学 |
| 开课时间: | 2013-09-27 |
| 课程语种: | 英语 |
| 中文简介: |  整合是一种社会影响，涉及为了适应群体而改变见解或行为。利用几个社交网络作为我们的实验数据的来源，我们研究了整合的影响如何在改变用户的在线行为中发挥作用。我们在个人，对等和小组级别上正式定义了几种主要的一致性类型。我们提出融合模型，以将社会整合的影响形式化为概率模型。融合可以区分和量化不同类型整合的影响。为了扩展到大型社交网络，我们提出了一种分布式学习方法，该方法可以在接近线性加速的情况下有效地构建Confluence模型。我们在Flickr，Gowalla，Weibo和Co Author的四种不同类型的大型社交网络上的实验结果验证了整合现象的存在。利用合规性信息，Confluence可以准确地预测用户的行为。我们的实验表明，与几种替代方法相比，Confluence可将预测准确度提高多达5 10％。 |
| 课程简介: | Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods. |
| 关 键 词: | 社交网络; 信息整合 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-05：zyk |
| 最后编审: | 2020-11-05：zyk |
| 阅读次数: | 45 |