2020
DOI: 10.1093/cz/zoaa050
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Using multilayer network analysis to explore the temporal dynamics of collective behavior

Abstract: Social organisms often show collective behaviours such as group foraging or movement. Collective behaviours can emerge from interactions between group members and may depend on the behaviour of key individuals. When social interactions change over time, collective behaviours may change because these behaviours emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over … Show more

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Cited by 12 publications
(6 citation statements)
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References 50 publications
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“…Layers are to that purpose typically generated on the basis of a series of (possibly overlapping) time windows and the nodes are linked only across sequential replicas to indicate identity. Mapping the temporal changes in connectivity patterns is meaningful in biomedical sciences as well ( 231 , 247 , 248 ) and could be very beneficial for the description of information flow and dynamic interaction patterns within the islets. More specifically, the activity patterns in these mini-organs are remarkably complex already under constant stimulation ( 147 ), and even more in a dynamic in vivo environment ( 67 , 249 , 250 ).…”
Section: Frontiers Of Islet Network Science: Assessing Multicellular ...mentioning
confidence: 99%
“…Layers are to that purpose typically generated on the basis of a series of (possibly overlapping) time windows and the nodes are linked only across sequential replicas to indicate identity. Mapping the temporal changes in connectivity patterns is meaningful in biomedical sciences as well ( 231 , 247 , 248 ) and could be very beneficial for the description of information flow and dynamic interaction patterns within the islets. More specifically, the activity patterns in these mini-organs are remarkably complex already under constant stimulation ( 147 ), and even more in a dynamic in vivo environment ( 67 , 249 , 250 ).…”
Section: Frontiers Of Islet Network Science: Assessing Multicellular ...mentioning
confidence: 99%
“…Understanding the layer dependencies in a multilayer network can inform the development of survey design, identify redundancies, or illuminate contextual connections. Moreover, the usefulness of finding latent structure in the layers motivates the use of latent-space models as a noise-free smoothing of the observed network, as proposed by Fisher (2021) [23]. As such, there is potential to use this work to understand layer dependence in a variety of applications where domain-specific knowledge can make use of the interpretations that the NNTuck provides.…”
Section: Discussionmentioning
confidence: 99%
“…We hope that this will serve as a basis for novel questions and for identifying the methodological challenges to come in order to determine the relative influence of each of these factors by integrating interaction networks across levels of organisation (Brandell et al, 2020; Jacoby & Freeman, 2016; Sueur et al., 2019). A particularly promising line of research is the development of multi‐layered networks (Fisher & Pinter‐Wollman, 2020; Mourier, Ledee, & Jacoby, 2019; Silk, Finn, Porter, & Pinter‐Wollman, 2018), hierarchically embedded interaction networks (Montiglio, Gotanda, Kratochwil, Laskowski, & Farine, 2020), or bipartite networks (Massol et al, 2020) that can expand exploration of interactions beyond social groups, spanning from cells to whole ecosystems, and their dynamics. For instance, Massol et al, 2020 used bipartite networks to analyse the structure of host‐microbiota interaction networks.…”
Section: Network Beyond Social Interactions: Cascading Effects Acrosmentioning
confidence: 99%