用于网络影响力预测的有监督秩聚合
Supervised Rank Aggregation for Predicting Influence in Networks
摘要：
社会网络分析的许多工作都集中在识别社会网络中最重要的参与者。这导致了一些影响力和权威的措施。虽然大多数这样的社会计量学（例如，PageRank）是由基于参与者在网络中的位置的直觉驱动的，要求“最有影响力”的参与者本身就是一个不适定的问题，除非它与特定的可测量任务放在一起。在给定领域中构建感兴趣的预测任务提供了一种定量比较不同影响措施的机制。此外，当我们知道要收集什么类型的可操作洞察力时，我们不需要依赖于单一的网络中心性度量。测量的组合更有可能捕获社交网络的各个方面，这些方面对于任务是预测性的和有益的。为此，我们提出了一种监督排名聚合的方法，由社会选择理论的技术驱动。我们通过在Twitter和引文网络上的实验说明了该方法的有效性。
英文摘要：
Much work in Social Network Analysis has focused on the identification of the most important actors in a social network. This has resulted in several measures of influence and authority. While most of such sociometrics (e.g., PageRank) are driven by intuitions based on an actors location in a network, asking for the "most influential" actors in itself is an ill-posed question, unless it is put in context with a specific measurable task. Constructing a predictive task of interest in a given domain provides a mechanism to quantitatively compare different measures of influence. Furthermore, when we know what type of actionable insight to gather, we need not rely on a single network centrality measure. A combination of measures is more likely to capture various aspects of the social network that are predictive and beneficial for the task. Towards this end, we propose an approach to supervised rank aggregation, driven by techniques from Social Choice Theory. We illustrate the effectiveness of this method through experiments on Twitter and citation networks.
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。
