Baboon (Papio anubis) social complexity--a network approach

Although many studies have analyzed the causes and consequences of social relationships, few studies have explicitly assessed how measures of social relationships are affected by the choice of behaviors used to quantify them. The use of many behaviors to measure social relationships in primates has long been advocated, but it was analytically difficult to implement this framework into primatological work. However, recent advances in social network analysis (SNA) now allow the comparison of multiple networks created from different behaviors. Here we use our database of baboon social behavior (Papio anubis, Gashaka Gumti National Park, Nigeria) to investigate (i) to what extent social networks created from different behaviors overlap, (ii) to what extent individuals occupy similar social positions in these networks and (iii) how sex affects social network position in this population of baboons. We used data on grooming, aggression, displacement, mounting and presenting, which were collected over a 15-month period. We calculated network parameters separately for each behavior. Networks based on displacement, mounting and presenting were very similar to each other, whereas grooming and aggression networks differed both from each other and from mounting, displacement and presenting networks. Overall, individual network positions were strongly affected by sex. Individuals central in one network tended to be central in most other networks as well, whereas other measures such as clustering coefficient were found to vary depending on the behavior analyzed. Thus, our results suggest that a baboon's social environment is best described by a multiplex network based on affiliative, aggressive and sexual behavior. Modern SNA provides a number of useful tools that will help us to better understand animals' social environment. We also discuss potential caveats related to their use.     

Social structure modulates the evolutionary consequences of social plasticity: A social network perspective on interacting phenotypes

Organisms express phenotypic plasticity during social interactions. Interacting phenotype theory has explored the consequences of social plasticity for evolution, but it is unclear how this theory applies to complex social structures. We adapt interacting phenotype models to general social structures to explore how the number of social connections between individuals and preference for phenotypically similar social partners affect phenotypic variation and evolution. We derive an analytical model that ignores phenotypic feedback and use simulations to test the predictions of this model. We find that adapting previous models to more general social structures does not alter their general conclusions but generates insights into the effect of social plasticity and social structure on the maintenance of phenotypic variation and evolution. Contribution of indirect genetic effects to phenotypic variance is highest when interactions occur at intermediate densities and decrease at higher densities, when individuals approach interacting with all group members, homogenizing the social environment across individuals. However, evolutionary response to selection tends to increase at greater network densities as the effects of an individual's genes are amplified through increasing effects on other group members. Preferential associations among similar individuals (homophily) increase both phenotypic variance within groups and evolutionary response to selection. Our results represent a first step in relating social network structure to the expression of social plasticity and evolutionary responses to selection.     

Hormones,behavior, and social network analysis: Exploring associations between cortisol,testosterone, and network structure

We used a new interdisciplinary paradigm of social network analysis (SNA) to investigate associations between hormones and social network structures. We examine these biobehavioral processes and test hypotheses about how hormones are associated with social network structures using exponential random graph modeling (ERGM) in a cohort of first-year students (n = 74; 93% female; M age = 27 years) from a highly competitive, accelerated nursing program. Participants completed friendship nominations and as a group simultaneously donated saliva (later assayed for cortisol and testosterone). ERGM analyses revealed that salivary cortisol levels were inversely associated with the number of outgoing ties (i.e., network activity). By contrast, testosterone was not related to friendship network structure. Integration of SNA and salivary bioscience creates a novel approach to understanding hormone–behavior relationships within the context of human social ecologies.     

Space to choose: network analysis of social preferences in a captive chimpanzee community, and implications for management

Social network analysis (SNA) is rapidly gaining popularity in primatology, but its application to the management of zoo-housed primates has been largely overlooked. Here I use SNA techniques to explore the social structure of chimpanzees (Pan troglodytes) housed in the new "Budongo Trail" exhibit at Edinburgh Zoo, UK. Given that individuals have extensive space (2332 m(2)), and access to several interconnected exhibit sections, I test the hypothesis that individuals are able to choose to interact with specific social partners. Spatial association and social interaction data were recorded from 400 focal watches on 11 individuals, and association, affiliative, and agonistic networks were constructed. Matrix correlations showed that individuals who spent time in close proximity were likely to affiliate with one another, but spatial association did not predict the frequency of agonistic encounters. Cluster analysis revealed significantly distinct sub-groups in the affiliative network (but not association or agonistic networks) in line with maternal kinship. Overall my findings support the hypothesis that the Budongo Trail exhibit facilitates the expression of social preferences, and suggests that SNA can be a useful tool to study zoo primates when proximity between individuals is not forced (i.e. in large, modern exhibits). Now that I have validated a set of SNA methods for this community, they can be used to trace changes in social dynamics over a longer time period, and ultimately assist zoo staff in their management decisions.     

Conformity and differentiation are two sides of the same coin

Variation between individuals is a key component of selection and hence evolutionary change. Social interactions are important drivers of variation, potentially making behaviour more similar (i.e., conform) or divergent (i.e., differentiate) between individuals. While documented across a wide range of animals, behaviours and contexts, conformity and differentiation are typically considered separately. Here, we argue that rather than independent concepts, they can be integrated onto a single scale that considers how social interactions drive changes in interindividual variance within groups: conformity reduces variance within groups while differentiation increases it. We discuss the advantages of placing conformity and differentiation at different ends of a single scale, allowing for a deeper understanding of the relationship between social interactions and interindividual variation.     

Using multilayer network analysis to explore the temporal dynamics of collective behavior

Social organisms often show collective behaviors such as group foraging or movement.Collective behaviors can emerge from interactions between group members and may depend on the behavior of key individuals.When social interactions change over time,collective behaviors may change because these behaviors 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 time or measure their stability.Furthermore,the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent.Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviors.We found that social interactions changed over time at a constant rate.Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed.Individuals that maintained a large and stable number of connections,despite changes in network structure,were the boldest individuals in the group.Therefore,social interactions and boldness are linked across time,but group collective behavior is not influenced by the stability of the social network.Our work demonstrates that dynamic social networks can be modeled in a multilayer framework.This approach may reveal biologically important temporal changes to social structure in other systems.     

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural 'knock-outs' in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.     

Understanding the Organization of Industrial Ecosystems

Industrial symbiosis (IS) has been used to describe the physical exchange and shared management of input and output materials by geographically proximate firms. Firms that engage in IS are said to belong to an industrial ecosystem. Symbiosis has been found to be motivated by economic considerations, such as lowering costs for waste disposal, as well as by environmental ones, such as accessing limited water supplies. Communication and trust among managers are thought to play important roles in exchanges; however, empirical studies have not been previously conducted. This study used social network analysis (SNA) to identify the prevalence of industrial symbiosis linkages in Barceloneta, Puerto Rico. The study quantified patterns in various relationships among firms and managers, including formal relations through supply chains, and informal ones through interpersonal interactions. SNA and statistical methods were used to explore how these ties correlate with observed industrial symbiosis activities. IS linkages were found to be less prevalent than product sales among firms and were concentrated among pharmaceutical firms at the core of the regional network. Trust among managers and position in the social hierarchy were found to be correlated with IS but not supply chain links. SNA was useful for examining the organization of different relationships in the industrial ecosystem, but contextual information is still needed to add meaning to its findings.     

A Behavioral Taxonomy of Loneliness in Humans and Rhesus Monkeys (Macaca mulatta)

Social relationships endow health and fitness benefits, but considerable variation exists in the extent to which individuals form and maintain salutary social relationships. The mental and physical health effects of social bonds are more strongly related to perceived isolation (loneliness) than to objective social network characteristics. We sought to develop an animal model to facilitate the experimental analysis of the development of, and the behavioral and biological consequences of, loneliness. In Study 1, using a population-based sample of older adults, we examined how loneliness was influenced both by social network size and by the extent to which individuals believed that their daily social interactions reflected their own choice. Results revealed three distinct clusters of individuals: (i) individuals with large networks who believed they had high choice were lowest in loneliness, (ii) individuals with small social networks who believed they had low choice were highest in loneliness, and (iii) the remaining two groups were intermediate and equivalent in loneliness. In Study 2, a similar three-group structure was identified in two separate samples of adult male rhesus monkeys (Macaca mulatta) living in large social groups: (i) those high in sociability who had complex social interaction with a broad range of social partners (putatively low in loneliness), (ii) those low in sociability who showed tentative interactions with certain classes of social partners (putatively high in loneliness), and (iii) those low in sociability who interacted overall at low levels with a broad range of social partners (putatively low or intermediate in loneliness). This taxonomy in monkeys was validated in subsequent experimental social probe studies. These results suggest that, in highly social nonhuman primate species, some animals may show a mismatch between social interest and social attainment that could serve as a useful animal model for experimental and mechanistic studies of loneliness.     

Network Effects on Scientific Collaborations

Background

The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly.

Methodology/Principal Findings

Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of ‘steel structure’ for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.

Conclusions/Significance

Authors’ network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.     

A social network analysis of treatment discoveries in cancer

Controlled clinical trials are widely considered to be the vehicle to treatment discovery in cancer that leads to significant improvements in health outcomes including an increase in life expectancy. We have previously shown that the pattern of therapeutic discovery in randomized controlled trials (RCTs) can be described by a power law distribution. However, the mechanism generating this pattern is unknown. Here, we propose an explanation in terms of the social relations between researchers in RCTs. We use social network analysis to study the impact of interactions between RCTs on treatment success. Our dataset consists of 280 phase III RCTs conducted by the NCI from 1955 to 2006. The RCT networks are formed through trial interactions formed i) at random, ii) based on common characteristics, or iii) based on treatment success. We analyze treatment success in terms of survival hazard ratio as a function of the network structures. Our results show that the discovery process displays power law if there are preferential interactions between trials that may stem from researchers' tendency to interact selectively with established and successful peers. Furthermore, the RCT networks are "small worlds": trials are connected through a small number of ties, yet there is much clustering among subsets of trials. We also find that treatment success (improved survival) is proportional to the network centrality measures of closeness and betweenness. Negative correlation exists between survival and the extent to which trials operate within a limited scope of information. Finally, the trials testing curative treatments in solid tumors showed the highest centrality and the most influential group was the ECOG. We conclude that the chances of discovering life-saving treatments are directly related to the richness of social interactions between researchers inherent in a preferential interaction model.     

Applying DNA computation to intractable problems in social network analysis

From ancient times to the present day, social networks have played an important role in the formation of various organizations for a range of social behaviors. As such, social networks inherently describe the complicated relationships between elements around the world. Based on mathematical graph theory, social network analysis (SNA) has been developed in and applied to various fields such as Web 2.0 for Web applications and product developments in industries, etc. However, some definitions of SNA, such as finding a clique, N-clique, N-clan, N-club and K-plex, are NP-complete problems, which are not easily solved via traditional computer architecture. These challenges have restricted the uses of SNA. This paper provides DNA-computing-based approaches with inherently high information density and massive parallelism. Using these approaches, we aim to solve the three primary problems of social networks: N-clique, N-clan, and N-club. Their accuracy and feasible time complexities discussed in the paper will demonstrate that DNA computing can be used to facilitate the development of SNA.     

Steady-state head-to-tail polymerization of actin or microtubules. II. Two-state and three-state kinetic cycles

In a previous paper, bioenergetic aspects of head-to-tail polymerization for a two-state actin ATPase cycle were discussed. In section 2, here, the steady-state polymer length distribution for this case is derived. The distribution has the same mathematical form as at equilibrium, but the parameters are different. In section 3, both bioenergetic topics and the polymer length distribution are considered for the more general and realistic case of a three-state actin ATPase cycle. Again, the mathematical form of the steady-state distribution is the same as at equilibrium, but the parameters are more complicated. In section 4, the question is examined of how much the mean and variance of a polymer length distribution, obtained from a finite experimental sample of polymer (aggregate) molecules, would be expected to deviate from the true mean and variance (from an infinite sample). Also considered briefly in section 4 is the effect of hard polymer-polymer interactions on the equilibrium polymer length distribution, at finite polymer concentrations.     

Tonic nicotinic transmission enhances spinal GABAergic presynaptic release and the frequency of spontaneous network activity

Synaptically driven spontaneous network activity (SNA) is observed in virtually all developing networks. Recurrently connected spinal circuits express SNA, which drives fetal movements during a period of development when GABA is depolarizing and excitatory. Blockade of nicotinic acetylcholine receptor (nAChR) activation impairs the expression of SNA and the development of the motor system. It is mechanistically unclear how nicotinic transmission influences SNA, and in this study we tested several mechanisms that could underlie the regulation of SNA by nAChRs. We find evidence that is consistent with our previous work suggesting that cholinergically driven Renshaw cells can initiate episodes of SNA. While Renshaw cells receive strong nicotinic synaptic input, we see very little evidence suggesting other spinal interneurons or motoneurons receive nicotinic input. Rather, we found that nAChR activation tonically enhanced evoked and spontaneous presynaptic release of GABA in the embryonic spinal cord. Enhanced spontaneous and/or evoked release could contribute to increased SNA frequency. Finally, our study suggests that blockade of nAChRs can reduce the frequency of SNA by reducing probability of GABAergic release. This result suggests that the baseline frequency of SNA is maintained through elevated GABA release driven by tonically active nAChRs. Nicotinic receptors regulate GABAergic transmission and SNA, which are critically important for the proper development of the embryonic network. Therefore, our results provide a better mechanistic framework for understanding the motor consequences of fetal nicotine exposure. © 2015 Wiley Periodicals, Inc. Develop Neurobiol 76: 298–312, 2016     

Evolution of Treatment Regimens in Multiple Myeloma: A Social Network Analysis

Background

Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM) to understand the patterns of competing treatment selections.

Methods

We used social network analysis (SNA) to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.

Results

135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four “steps” within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.

Conclusion

Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.     

Social traits,social networks and evolutionary biology

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals’ network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group‐level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes.     

Network analysis as a tool to understand social development in spider monkeys

The emerging field of network science has demonstrated that an individual's connectedness within their social network has cascading effects to other dimensions of life. Like humans, spider monkeys live in societies with high fission–fusion dynamics, and are remarkably social. Social network analysis (SNA) is a powerful tool for quantifying connections that may vary as a function of initiating or receiving social behaviors, which has been described as shifting social roles. In primatology, the SNA literature is dominated by work in catarrhines, and has yet to be applied to the study of development in a platyrrhine model. Here, SNA was utilized in combination with R-Index social role calculation to characterize social interaction patterns in juvenile and adult Colombian spider monkeys (Ateles fusciceps rufiventris). Connections were examined across five behaviors: embrace, face-embrace, grooming, agonism, and tail-wrapping from 186 hr of observation and four network metrics. Mann–Whitney U tests were utilized to determine differences between adult and juvenile social network patterns for each behavior. Face-embrace emerged as the behavior with different network patterns for adults and juveniles for every network metric. With regard to social role, juveniles were receivers, not initiators, for embrace, face-embrace, and grooming (ps < .05). Network and social role differences are discussed in light of social development and aspects of the different behaviors.     

Time-ordered networks reveal limitations to information flow in ant colonies

Background

An important function of many complex networks is to inhibit or promote the transmission of disease, resources, or information between individuals. However, little is known about how the temporal dynamics of individual-level interactions affect these networks and constrain their function. Ant colonies are a model comparative system for understanding general principles linking individual-level interactions to network-level functions because interactions among individuals enable integration of multiple sources of information to collectively make decisions, and allocate tasks and resources.

Methodology/Findings

Here we show how the temporal and spatial dynamics of such individual interactions provide upper bounds to rates of colony-level information flow in the ant Temnothorax rugatulus. We develop a general framework for analyzing dynamic networks and a mathematical model that predicts how information flow scales with individual mobility and group size.

Conclusions/Significance

Using thousands of time-stamped interactions between uniquely marked ants in four colonies of a range of sizes, we demonstrate that observed maximum rates of information flow are always slower than predicted, and are constrained by regulation of individual mobility and contact rate. By accounting for the ordering and timing of interactions, we can resolve important difficulties with network sampling frequency and duration, enabling a broader understanding of interaction network functioning across systems and scales.