Equine and Animal Cognition Reference Database -- Query Results

Hasenjager, M. J., & Dugatkin, L. A. Social Network Analysis in Behavioral Ecology. Advances in the Study of Behavior. Academic Press. Abstract: Abstract In recent years, behavioral ecologists have embraced social network analysis (SNA) in order to explore the structure of animal societies and the functional consequences of that structure. We provide a conceptual introduction to the field that focuses on historical developments, as well as on novel insights generated by recent work. First, we discuss major advances in the analysis of nonhuman societies, culminating in the use of SNA by behavioral ecologists. Next, we discuss how network-based approaches have enhanced our understanding of social structure and behavior over the past decade, focusing on: (1) information transmission, (2) collective behaviors, (3) animal personality, and (4) cooperation. These behaviors and phenomena possess several featuresÂ—e.g., indirect effects, emergent propertiesÂ—that network analysis is well equipped to handle. Finally, we highlight recent developments in SNA that are allowing behavioral ecologists to address increasingly sophisticated questions regarding the structure and function of animal sociality. Keywords: Animal personalities; Animal social networks; Collective behavior; Cooperation; Dynamic networks; Emergent properties; Network theory; Social behavior; Social learning; Social structure https://references.equine-behaviour.de/show.php?record=5863
Newman, M. E. J. (2003). The Structure and Function of Complex Networks. SIAM Rev., 45(2), 167–256. Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks. Keywords: networks; graph theory; complex systems; computer networks; social networks; random graphs; percolation theory https://references.equine-behaviour.de/show.php?record=5214

Abstract: Abstract In recent years, behavioral ecologists have embraced social network analysis (SNA) in order to explore the structure of animal societies and the functional consequences of that structure. We provide a conceptual introduction to the field that focuses on historical developments, as well as on novel insights generated by recent work. First, we discuss major advances in the analysis of nonhuman societies, culminating in the use of SNA by behavioral ecologists. Next, we discuss how network-based approaches have enhanced our understanding of social structure and behavior over the past decade, focusing on: (1) information transmission, (2) collective behaviors, (3) animal personality, and (4) cooperation. These behaviors and phenomena possess several featuresÂ—e.g., indirect effects, emergent propertiesÂ—that network analysis is well equipped to handle. Finally, we highlight recent developments in SNA that are allowing behavioral ecologists to address increasingly sophisticated questions regarding the structure and function of animal sociality.

Keywords: Animal personalities; Animal social networks; Collective behavior; Cooperation; Dynamic networks; Emergent properties; Network theory; Social behavior; Social learning; Social structure

https://references.equine-behaviour.de/show.php?record=5863

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.