Confluence: conformity influence in large social networks

Jie Tang, Sen Wu, Jimeng Sun
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引用次数: 133

Abstract

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.
合流:大型社会网络中的从众影响
从众是一种社会影响,包括为了适应群体而改变意见或行为。我们采用几个社交网络作为实验数据的来源,研究从众效应如何在改变用户的在线行为中发挥作用。我们在个人、同伴和群体层面正式定义了几种主要的从众类型。我们提出合流模型,将社会从众的影响形式化为一个概率模型。合流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,该方法可以有效地构建Confluence模型,并且具有近线性加速。我们在四种不同类型的大型社交网络Flickr、Gowalla、Weibo和合作者上的实验结果验证了从众现象的存在。通过整合信息,Confluence可以准确预测用户的行为。我们的实验表明,与几种替代方法相比,Confluence显著提高了5-10%的预测精度。
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