Social networks and the development of social skills in cowbirds

The complex interrelationships among individuals within social environments can exert selection pressures on social skills: those behaviours and cognitive processes that allow animals to manipulate and out-reproduce others. Social complexity can also have a developmental effect on social skills by providing individuals with opportunities to hone their skills by dealing with the challenges posed in within-group interactions. We examined how social skills develop in captive, adult male brown-headed cowbirds (Molothrus ater) that were exposed to differing levels of 'social complexity' across a 2-year experiment. After each year, subjects housed in groups with dynamic social structure (where many individuals entered and exited the groups during the year) outcompeted birds who had been housed in static groups. Exposure to dynamic structure subsequently led to substantial changes to the social networks of the home conditions during the breeding season. Static groups were characterized by a predictable relationship between singing and reproductive success that was stable across years. In dynamic conditions, however, males showed significant variability in their dominance status, their courting and even in their mating success. Reproductive success of males varied dramatically across years and was responsive to social learning in adulthood, and socially dynamic environments 'trained' individuals to be better competitors, even at an age when the development of many traits important for breeding (like song quality) had ended.     

It's the recipient that counts: spending money on strong social ties leads to greater happiness than spending on weak social ties

Previous research has shown that spending money on others (prosocial spending) increases happiness. But, do the happiness gains depend on who the money is spent on? Sociologists have distinguished between strong ties with close friends and family and weak ties—relationships characterized by less frequent contact, lower emotional intensity, and limited intimacy. We randomly assigned participants to reflect on a time when they spent money on either a strong social tie or a weak social tie. Participants reported higher levels of positive affect after recalling a time they spent on a strong tie versus a weak tie. The level of intimacy in the relationship was more important than the type of relationship; there was no significant difference in positive affect after recalling spending money on a family member instead of a friend. These results add to the growing literature examining the factors that moderate the link between prosocial behaviour and happiness.     

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.     

Assessing vaccination sentiments with online social media: implications for infectious disease dynamics and control

There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC-estimated vaccination rates by region. Analysis of the network of opinionated users showed that information flows more often between users who share the same sentiments - and less often between users who do not share the same sentiments - than expected by chance alone. We also found that most communities are dominated by either positive or negative sentiments towards the novel vaccine. Simulations of infectious disease transmission show that if clusters of negative vaccine sentiments lead to clusters of unprotected individuals, the likelihood of disease outbreaks is greatly increased. Online social media provide unprecedented access to data allowing for inexpensive and efficient tools to identify target areas for intervention efforts and to evaluate their effectiveness.     

Social ties at work: Roma migrants and the community dynamics

This research examines how the internal social dynamics of Roma communities at home shape their propensity to migrate. It is theoretically grounded in the literature on social capital and focuses on two core concepts: ‘migration-rich’ and ‘migration-poor’ communities. The research is based on in-depth interviews and informal discussions with Roma from six (mainly rural) communities of Transylvania (Romania) and includes qualitative data gathered from migrants as well as from people who did not migrate. The findings challenge existing conceptualizations of Roma migration as either explained by poverty alone or by cultural arguments (such as nomadism). This paper indicates that even in the context of severe poverty, social networks are actually decisive for migration. It demonstrates that the patterns of migration tend to be community-specific and shaped by a locally shared culture (ethos) on migration. The research suggests policy choices according to the community profile and its internal dynamics.     

Analysis of context dependence in social interaction networks of a massively multiplayer online role-playing game

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.     

Structural and dynamical patterns on online social networks: the Spanish May 15th movement as a case study

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics.     

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

Background

Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges.

Results

To provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes.

Conclusions

We proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes.

     

Personality in the context of social networks

There is great interest in environmental effects on the development and evolution of animal personality traits. An important component of an individual's environment is its social environment. However, few studies look beyond dyadic relationships and try to place the personality of individuals in the context of a social network. Social network analysis provides us with many new metrics to characterize the social fine-structure of populations and, therefore, with an opportunity to gain an understanding of the role that different personalities play in groups, communities and populations regarding information or disease transmission or in terms of cooperation and policing of social conflicts. The network position of an individual is largely a consequence of its interactive strategies. However, the network position can also shape an individual's experiences (especially in the case of juveniles) and therefore can influence the way in which it interacts with others in future. Finally, over evolutionary time, the social fine-structure of animal populations (as quantified by social network analysis) can have important consequences for the evolution of personalities-an approach that goes beyond the conventional game-theoretic analyses that assumed random mixing of individuals in populations.     

Human communication dynamics in digital footsteps: a study of the agreement between self-reported ties and email networks

Digital communication data has created opportunities to advance the knowledge of human dynamics in many areas, including national security, behavioral health, and consumerism. While digital data uniquely captures the totality of a person's communication, past research consistently shows that a subset of contacts makes up a person's "social network" of unique resource providers. To address this gap, we analyzed the correspondence between self-reported social network data and email communication data with the objective of identifying the dynamics in e-communication that correlate with a person's perception of a significant network tie. First, we examined the predictive utility of three popular methods to derive social network data from email data based on volume and reciprocity of bilateral email exchanges. Second, we observed differences in the response dynamics along self-reported ties, allowing us to introduce and test a new method that incorporates time-resolved exchange data. Using a range of robustness checks for measurement and misreporting errors in self-report and email data, we find that the methods have similar predictive utility. Although e-communication has lowered communication costs with large numbers of persons, and potentially extended our number of, and reach to contacts, our case results suggest that underlying behavioral patterns indicative of friendship or professional contacts continue to operate in a classical fashion in email interactions.     

The influence of body weight on social network ties among adolescents

Evidence of negative stereotypes, prejudice and discrimination towards obese individuals has been widely documented. However, the effect of a larger body size on social network ties or friendship formations is less well understood. In this paper, we explore the extent to which higher body weight results in social marginalization of adolescents. Using data from a nationally representative sample of adolescents, we estimate endogeneity-corrected models including school-level fixed effects that account for bi-directionality and unobserved confounders to ascertain the effect of body weight on social network ties. We find that obese adolescents have fewer friends and are less socially integrated than their non-obese counterparts. We also find that such penalties in friendship networks are present among whites but not African-Americans or Hispanics, with the largest effect among white females. These results are robust to common environmental influences at the school-level and to controls for preferences, risk attitudes, low self-esteem and objective measures of physical attractiveness.     

Social networks in fish

Social network theory underwent rapid development in the 1970s by sociologists and was recently further refined by physicists. Network theory has been applied to systems consisting of interconnected components as different as that of actors in Hollywood, the neural network of the nematode worm Caenorhabditis elegans, and the US national power grid. Network theory has great potential as a conceptual tool, for example, it can be used to provide quantitative predictions regarding information transmission, spreading of diseases, and the potential for establishing reciprocal altruistic relationships. Conventionally behavioural studies have been restricted to the analysis of social interactions between isolated pairs or small groups of individuals. Here we built up, however, from pair‐wise interactions, the social network of an entire population of guppies (Poecilia reticulata) in Trinidad. We show that guppies have highly structured social networks with significant repeated interactions between male–male, female–female and male–female pairs. Furthermore, sexual segregation within the network is positively correlated with the strength of the social interactions. Finally, individuals were observed to be more variable in their inter‐sexual interactions than their intra‐sexual ones.     

Detecting social transmission in networks

In recent years researchers have drawn attention to a need for new methods with which to identify the spread of behavioural innovations through social transmission in animal populations. Network-based analyses seek to recognise diffusions mediated by social learning by detecting a correspondence between patterns of association and the flow of information through groups. Here we introduce a new order of acquisition diffusion analysis (OADA) and develop established time of acquisition diffusion analysis (TADA) methods further. Through simulation we compare the merits of these and other approaches, demonstrating that OADA and TADA have greater power and lower Type I error rates than available alternatives, and specifying when each approach should be deployed. We illustrate the new methods by applying them to reanalyse an established dataset corresponding to the diffusion of foraging innovations in starlings, where OADA and TADA detect social transmission that hitherto had been missed. The methods are potentially widely applicable by researchers wishing to detect social learning in natural and captive populations of animals, and to facilitate this we provide code to implement OADA and TADA in the statistical package R.     

Trust transitivity in social networks

Non-centralized recommendation-based decision making is a central feature of several social and technological processes, such as market dynamics, peer-to-peer file-sharing and the web of trust of digital certification. We investigate the properties of trust propagation on networks, based on a simple metric of trust transitivity. We investigate analytically the percolation properties of trust transitivity in random networks with arbitrary in/out-degree distributions, and compare with numerical realizations. We find that the existence of a non-zero fraction of absolute trust (i.e. entirely confident trust) is a requirement for the viability of global trust propagation in large systems: The average pair-wise trust is marked by a discontinuous transition at a specific fraction of absolute trust, below which it vanishes. Furthermore, we perform an extensive analysis of the Pretty Good Privacy (PGP) web of trust, in view of the concepts introduced. We compare different scenarios of trust distribution: community- and authority-centered. We find that these scenarios lead to sharply different patterns of trust propagation, due to the segregation of authority hubs and densely-connected communities. While the authority-centered scenario is more efficient, and leads to higher average trust values, it favours weakly-connected "fringe" nodes, which are directly trusted by authorities. The community-centered scheme, on the other hand, favours nodes with intermediate in/out-degrees, in detriment of the authorities and its "fringe" peers.