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Found 6 papers, 5 papers with code
Unifying Pairwise Interactions in Complex Dynamics
Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems.
Inferring network properties from time series via transfer entropy and mutual information: validation of bivariate versus multivariate approaches
Functional and effective networks inferred from time series are at the core of network neuroscience.
Neurons and Cognition Information Theory Social and Information Networks Information Theory Data Analysis, Statistics and Probability
Assessing the Significance of Directed and Multivariate Measures of Linear Dependence Between Time Series
Inferring linear dependence between time series is central to our understanding of natural and artificial systems.
Methodology Information Theory Information Theory Statistics Theory Data Analysis, Statistics and Probability Neurons and Cognition Applications Statistics Theory
Deriving pairwise transfer entropy from network structure and motifs
The pairwise (or bivariate) transfer entropy from a source to a target node in a network does not depend solely on the local source-target link weight, but on the wider network structure that the link is embedded in.
Information Theory Social and Information Networks Information Theory Data Analysis, Statistics and Probability Neurons and Cognition
JIDT: An information-theoretic toolkit for studying the dynamics of complex systems
We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data.
Information Theory Mathematical Software Social and Information Networks Information Theory Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability 94A15
Information Transfer in Swarms with Leaders
Depending upon the leadership model, leaders can use their external information either all the time or in response to local conditions [Couzin et al. 2005; Sun et al. 2013].
