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Asymmetry through time dependency

Open Access
Regular Article
Part of the following topical collections:
  1. Topical issue: Temporal Network Theory and Applications

Abstract

Given a single network of interactions, asymmetry arises when the links are directed. For example, if protein A upregulates protein B and protein B upregulates protein C, then (in the absence of any further relationships between them) A may affect C but not vice versa. This type of imbalance is reflected in the associated adjacency matrix, which will lack symmetry. A different type of imbalance can arise when interactions appear and disappear over time. If A meets B today and B meets C tomorrow, then (in the absence of any further relationships between them) A may pass a message or disease to C, but not vice versa. Hence, even when each interaction is a two-way exchange, the effect of time ordering can introduce asymmetry. This observation is very closely related to the fact that matrix multiplication is not commutative. In this work, we describe a method that has been designed to reveal asymmetry in static networks and show how it may be combined with a measure that summarizes the potential information flow between nodes in the temporal case. This results in a new method that quantifies the asymmetry arising through time ordering. We show by example that the new tool can be used to visualize and quantify the amount of asymmetry caused by the arrow of time.

Keywords

Adjacency Matrix Directed Edge Dynamic Walk Fiedler Vector Inherent Asymmetry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2016

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUniversity of StrathclydeGlasgowUK

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