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Temporal dynamics and network analysis
Author(s) -
Blonder Benjamin,
Wey Tina W.,
Dornhaus Anna,
James Richard,
Sih Andrew
Publication year - 2012
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/j.2041-210x.2012.00236.x
Subject(s) - computer science , field (mathematics) , data science , network dynamics , evolutionary dynamics , extant taxon , network analysis , ecological network , key (lock) , network science , ecology , complex network , management science , biology , evolutionary biology , population , sociology , world wide web , pure mathematics , economics , physics , demography , mathematics , computer security , discrete mathematics , quantum mechanics , ecosystem
Summary 1. Network analysis is widely used in diverse fields and can be a powerful framework for studying the structure of biological systems. Temporal dynamics are a key issue for many ecological and evolutionary questions. These dynamics include both changes in network topology and flow on the network. Network analyses that ignore or do not adequately account for the temporal dynamics can result in inappropriate inferences. 2. We suggest that existing methods are currently under‐utilized in many ecological and evolutionary network analyses and that the broader incorporation of these methods will considerably advance the current field. Our goal is to introduce ecologists and evolutionary biologists interested in studying network dynamics to extant ideas and methodological approaches, at a level appropriate for those new to the field. 3. We present an overview of time‐ordered networks, which provide a framework for analysing network dynamics that addresses multiple inferential issues and permits novel types of temporally informed network analyses. We review available methods and software, discuss the utility and considerations of different approaches, provide a worked example analysis and highlight new research opportunities in ecology and evolutionary biology.