Exploring animal behaviour multilayer networks in immersive environments – a conceptual framework-Reference-Cited by-同舟云学术

Exploring animal behaviour multilayer networks in immersive environments – a conceptual framework

Published:2024-07-24 Issue: Volume: Page:
ISSN:1613-4516
Container-title:Journal of Integrative Bioinformatics
language:en
Short-container-title:

Author:

Feyer Stefan Paul¹^ORCID,Pinaud Bruno²^ORCID,Klein Karsten¹^ORCID,Lein Etienne³^ORCID,Schreiber Falk¹⁴^ORCID

Affiliation:

1. Department of Computer and Information Science , 26567 University of Konstanz , Konstanz , Germany

2. University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800 , Bordeaux , France

3. Behavioural Evolution Lab , Max Planck Institute of Animal Behavior , Konstanz , Germany

4. Faculty of Information Technology , Monash University , Clayton , Australia

Abstract

Abstract Animal behaviour is often modelled as networks, where, for example, the nodes are individuals of a group and the edges represent behaviour within this group. Different types of behaviours or behavioural categories are then modelled as different yet connected networks which form a multilayer network. Recent developments show the potential and benefit of multilayer networks for animal behaviour research as well as the potential benefit of stereoscopic 3D immersive environments for the interactive visualisation, exploration and analysis of animal behaviour multilayer networks. However, so far animal behaviour research is mainly supported by libraries or software on 2D desktops. Here, we explore the domain-specific requirements for (stereoscopic) 3D environments. Based on those requirements, we provide a proof of concept to visualise, explore and analyse animal behaviour multilayer networks in immersive environments.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/jib-2024-0022/pdf

Reference54 articles.

1. Nicholson, DN, Greene, CS. Constructing knowledge graphs and their biomedical applications. Comput Struct Biotechnol J 2020;18:1414–28. https://doi.org/10.1016/j.csbj.2020.05.017.

2. Junker, BH, Schreiber, F. Analysis of biological networks. Wiley series on bioinformatics, computational techniques and engineering. Weinheim, Deutschland: Wiley; 2008.

3. Wey, T, Blumstein, DT, Shen, W, Jordán, F. Social network analysis of animal behaviour: a promising tool for the study of sociality. Anim Behav 2008;75:333–44. https://doi.org/10.1016/j.anbehav.2007.06.020.

4. Dickison, ME, Magnani, M, Rossi, L. Multilayer social networks. Cambridge: Cambridge University Press; 2016.

5. McGee, F, Ghoniem, M, Melançon, G, Otjacques, B, Pinaud, B. The state of the art in multilayer network visualization. Comput Graph Forum 2019;38:125–49. https://doi.org/10.1111/cgf.13610.