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The fundamental building block of social influence is for one person to elicit a response in another. Researchers measuring a "response" in social media typically depend either on detailed models of human behavior or on platform-specific cues such as re-tweets, hash tags, URLs, or mentions. Most content on social networks is difficult to model because the(More)
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(More)
Estimating mutual information (MI) from samples is a fundamental problem in statistics, machine learning, and data analysis. Recently it was shown that a popular class of non-parametric MI estimators perform very poorly for strongly dependent variables and have sample complexity that scales exponentially with the true MI. This undesired behavior was(More)
Information theory provides a powerful set of tools for discovering relationships among variables with minimal assumptions. Social media platforms provide a rich source of information than can include temporal, spatial, textual, and network information. What are the interesting information theoretic measures for social media and how can we estimate these(More)
We consider the problem of developing data-driven proba-bilistic models describing the activity profile of users in on-line social network settings. Previous models of user activities have discarded the potential influence of a user's network structure on his temporal activity patterns. Here we address this shortcoming and suggest an alternative approach(More)
We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective. Intuitively, the optimization searches for a set of latent factors that best explain the correlations in the data as measured by multivariate mutual information. The method is unsupervised, requires(More)
We consider a range of " theories " that violate the uncertainty relation for anti-commuting observables derived in [JMP, 49, 062105 (2008)]. We first show that Tsirelson's bound for the CHSH inequality can be derived from this uncertainty relation, and that relaxing this relation allows for non-local correlations that are stronger than what can be obtained(More)