社交网络中偏好传播的抽样偏好聚合模型
Modeling Spread of Preferences in Social Networks for Sampling-based Preference Aggregation
摘要：
在人口众多的情况下，收集个人对一组备选方案的偏好并得出总体或集体偏好是一项密集的任务。我们表明，社会网络的人口基础可以利用有效地完成这一任务，通过采样的一个小的子集的代表性节点的偏好。我们首先开发了一个Facebook应用程序来创建一个由节点偏好和底层社交网络组成的数据集，使用该数据集，我们开发了一个模型，该模型可以捕获偏好在典型社交网络中的节点之间的分布情况。因此，我们提出了一个适当的目标函数的问题，选择最佳代表节点。我们设计了两个算法，即，Greedy-min提供了一个广泛的流行的投票规则类的性能保证，和Greedy-sum在实践中表现出优异的性能。我们比较了这些算法对随机轮询和流行的中心性措施的性能，并提供了一个详细的分析所获得的结果。我们的分析表明，使用社交网络信息选择代表有利于聚合与个人话题相关的偏好（例如，生活方式），而具有合理样本大小的随机轮询足以聚集与社会话题相关的偏好（例如，政府政策）。
英文摘要：
Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We compare the performance of these proposed algorithms against random-polling and popular centrality measures, and provide a detailed analysis of the obtained results. Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
