Next Article in Journal
Multiscale Compression Entropy of Microvascular Blood FlowSignals: Comparison of Results from Laser Speckle Contrastand Laser Doppler Flowmetry Data in Healthy Subjects
Next Article in Special Issue
A Recipe for the Estimation of Information Flow in a Dynamical System
Previous Article in Journal
Sensitivity Analysis for Urban Drainage Modeling Using Mutual Information
Previous Article in Special Issue
Structure of a Global Network of Financial Companies Based on Transfer Entropy
Open AccessArticle

Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy

Automation School, Beijing University of Posts and Telecommunications, Beijing 100876, China
Entropy 2014, 16(11), 5753-5776;
Received: 19 August 2014 / Revised: 5 October 2014 / Accepted: 28 October 2014 / Published: 3 November 2014
(This article belongs to the Special Issue Transfer Entropy)
Topological measures are crucial to describe, classify and understand complex networks. Lots of measures are proposed to characterize specific features of specific networks, but the relationships among these measures remain unclear. Taking into account that pulling networks from different domains together for statistical analysis might provide incorrect conclusions, we conduct our investigation with data observed from the same network in the form of simultaneously measured time series. We synthesize a transfer entropy-based framework to quantify the relationships among topological measures, and then to provide a holistic scenario of these measures by inferring a drive-response network. Techniques from Symbolic Transfer Entropy, Effective Transfer Entropy, and Partial Transfer Entropy are synthesized to deal with challenges such as time series being non-stationary, finite sample effects and indirect effects. We resort to kernel density estimation to assess significance of the results based on surrogate data. The framework is applied to study 20 measures across 2779 records in the Technology Exchange Network, and the results are consistent with some existing knowledge. With the drive-response network, we evaluate the influence of each measure by calculating its strength, and cluster them into three classes, i.e., driving measures, responding measures and standalone measures, according to the network communities. View Full-Text
Keywords: network inference; topological measures; transfer entropy network inference; topological measures; transfer entropy
MDPI and ACS Style

Ai, X. Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy. Entropy 2014, 16, 5753-5776.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Search more from Scilit
Back to TopTop