Recent research has explored the increasingly important role of social media
by examining the dynamics of individual and group behavior, characterizing
patterns of information diffusion, and identifying influential individuals. In
this paper we suggest a measure of causal relationships between nodes based on
the information-theoretic notion of transfer entropy, or information transfer.
This theoretically grounded measure is based on dynamic information, captures
fine-grain notions of influence, and admits a natural, predictive
interpretation. Causal networks inferred by transfer entropy can differ
significantly from static friendship networks because most friendship links are
not useful for predicting future dynamics. We demonstrate through analysis of
synthetic and real-world data that transfer entropy reveals meaningful hidden
network structures. In addition to altering our notion of who is influential,
transfer entropy allows us to differentiate between weak influence over large
groups and strong influence over small groups.

本文通过应用信息传递的转移熵概念，提出了一种测量节点之间因果关系的理论基础的方法。通过分析合成和真实世界数据，证明了转移熵可以揭示有意义的隐藏网络结构，并能够区分大群体的弱影响和小团体的强影响。