Mastodawn

Iacopo Iacopini Jun 7, 2024

Do you work at the intersection of complexity science and animal behaviour? 🐦🐒🐭🐝🐜🐙🦋

Don't miss your chance to present your interdisciplinary research at CRAB🦀! There are still 10d left to submit an abstract and join us in Exeter at CCS2024.

https://sites.google.com/view/crab2024/home

CRAB2024

Complexity Research in Animal Behaviour Satellite @ CCS 2024 - Exeter, September 2024 Animal groups can be considered as complex systems of interacting entities (the individuals). The collective behaviour of animal groups is an emergent outcome of the traits of individuals within the group and

Iacopo Iacopini Mar 11, 2024

Funded PhD alert! 🚨

Interested in joining me at the Network Science Institute - London campus @Northeastern University London with a fully funded PhD Scholarship in Network Science?

Call: tinyurl.com/3fxe9j4f
Deadline: 1 April 2024
Start date: 1 October 2024

Just get in touch | Pls help spread the word 🕸️🕸️

Iacopo Iacopini Sep 13, 2023

🐦 New preprint on higher-order network approaches to study animal (vocal) communication 🐦 : http://arxiv.org/abs/2309.03783

Great teamwork led by Matthew Silk w/ Jennifer Foote, Nina Fefferman & Elizabeth Derryberry

Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication

Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalisation. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual's behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g., hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: a) alter predictions made about the outcome of vocally-coordinated group departures; b) generate different patterns of song synchronisation than models that only include dyadic interactions; and c) inform models of cultural evolution of vocal communication. Together, our examples highlight the potential power of higher-order networks to study animal vocal communication. We then build on our case studies to identify key challenges in applying higher-order network approaches in this context and outline important research questions these techniques could help answer.

arXiv.org

Iacopo Iacopini Jun 20, 2023

How do groups form and develop? How do people move between different groups?

With @martonkarsai & @alainbarrat we studied the temporal dynamics of group interactions in social networks and proposed a new hypergraph model!

Arxiv 👉 https://arxiv.org/abs/2306.09967
5m recap 👉 https://iaciac.github.io/post/temp-dyn-groups/

The temporal dynamics of group interactions in higher-order social networks

Representing social systems as networks, starting from the interactions between individuals, sheds light on the mechanisms governing their dynamics. However, networks encode only pairwise interactions, while most social interactions occur among groups of individuals, requiring higher-order network representations. Despite the recent interest in higher-order networks, little is known about the mechanisms that govern the formation and evolution of groups, and how people move between groups. Here, we leverage empirical data on social interactions among children and university students to study their temporal dynamics at both individual and group levels, characterising how individuals navigate groups and how groups form and disaggregate. We find robust patterns across contexts and propose a dynamical model that closely reproduces empirical observations. These results represent a further step in understanding social systems, and open up research directions to study the impact of group dynamics on dynamical processes that evolve on top of them.

arXiv.org