[MARMAM] New publication on dolphin social networks

Robert Rankin robertw.rankin at gmail.com
Fri Feb 12 07:14:16 PST 2016


Dear MARMAM'ers,

We invite you to read our new publication on bottlenose dolphin social
networks. We studied a variety of weighted metrics (centrality,
transitivity, affinity) and argue that many so-called "network" metrics are
highly redundant to simple individual-attributes, and are not informative
of dolphin social structure. We also compare how social networks' weight
information (strength of connections) and binary information (who is
connected to who) are differently important for inferences about
community-partitioning and network structure.

Rankin, RW, J Mann, L Singh, EM Patterson, E Krzyszczyk, and L Bejder.
2016. The role of weighted and topological network information to
understand animal social networks: a null model approach. Animal Behaviour
113:215–228. DOI:10.1016/j.anbehav.2015.12.015

ABSTRACT:
Network null models are important to drawing conclusions about individual-
and population-(or graph) level metrics. While the null models of binary
networks are well studied, recent literature on weighted networks suggests
that: (1) many so-called ‘weighted metrics’ do not actually depend on
weights, and (2) many metrics that supposedly measure higher-order social
structure actually are highly correlated with individual-level attributes.
This is important for behavioural ecology studies where weighted network
analyses predominate, but there is no consensus on how null models should
be specified. Using real social networks, we developed three null models
that address two technical challenges in the networks of social animals:
(1) how to specify null models that are suitable for ‘proportion-weighted
networks’ based on indices such as the half-weight index; and (2) how to
condition on the degree- and strength-sequence and both. We compared 11
metrics with each other and against null-model expectations for 10 social
networks of bottlenose dolphin, Tursiops aduncus, from Shark Bay,
Australia. Observed metric values were similar to null-model expectations
for some weighted metrics, such as centrality measures, disparity and
connectivity, whereas other metrics such as affinity and clustering were
informative about dolphin social structure. Because weighted metrics can
differ in their sensitivity to the degree-sequence or strength-sequence,
conditioning on both is a more reliable and conservative null model than
the more common strength-preserving null-model for weighted networks. Other
social structure analyses, such as community partitioning by weighted
Modularity optimization, were much less sensitive to the underlying
null-model. Lastly, in contrast to results in other scientific disciplines,
we found that many weighted metrics do not depend trivially on topology;
rather, the weight distribution contains important information about
dolphin social structure.

Download the free PDF here: http://authors.elsevier.com/a/1SWvl_4tkzCKe
Visit on Mendeley: http://mnd.ly/1QwiaR3
Visit on ResearchGate:
http://www.researchgate.net/publication/294088685_The_role_of_weighted_and_topological_network_information_to_understand_animal_social_networks_a_null_model_approach

Import the following bibtex into your reference manager:
@article{rankin_networks_2016,
title = {The role of weighted and topological network information to
understand animal social networks: a null model approach},
author = {Rankin, Robert W. and Mann, Janet and Singh, Lisa and Patterson,
Eric M. and Krzyszczyk, Ewa and Bejder, Lars},
journal = {Animal Behaviour},
year = {2016},
pages = {215--228},
volume = {113},
doi = {10.1016/j.anbehav.2015.12.015},
url = {http://www.sciencedirect.com/science/article/pii/S000334721500456X},
keywords = {bias, bottlenose dolphin, community structure, maximum entropy,
network topology, social network}
}



-- 
"You could give Aristotle a tutorial. And you could thrill him to the core
of his being ... Such is the privilege of living after Newton, Darwin,
Einstein, Planck, Watson, Crick and their colleagues."
-- Richard Dawkins
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