This article introduces item response models for rating relational data. The relational data are obtained via ratings of senders and receivers in a directed network. The proposed models allow comparisons of senders and receivers on a one-dimensional latent scale while accounting for unobserved homophilic relationships. We show that the approach effectively captures reciprocity and clustering phenomena in the relational data. We estimate model parameters using a Bayesian specification and utilize Markov Chain Monte Carlo methods to approximate the full conditional posterior distributions. Simulation studies demonstrate that model parameters can be recovered satisfactorily even when the dimensionality of the network is small. We also present an extensive empirical application to illustrate the usefulness of the proposed models for complete and incomplete networks.

Stochastic actor-oriented models (SAOMs) were designed in the social network setting to capture network dynamics representing a variety of influences on network change. The standard framework assumes the observed networks are free of false positive and false negative edges, which may be an unrealistic assumption. We propose a hidden Markov model (HMM) extension to these models, consisting of two components: 1) a latent model, which assumes that the unobserved, true networks evolve according to a Markov process as they do in the SAOM framework; and 2) a measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for parameter estimation. We address the computational challenge posed by a massive discrete state space, of a size exponentially increasing in the number of vertices, through the use of the missing information principle and particle filtering. We present results from a simulation study, demonstrating our approach offers improvement in accuracy of estimation, in contrast to the standard SAOM, when the underlying networks are observed with noise. We apply our method to functional brain networks inferred from electroencephalogram data, revealing larger effect sizes when compared to the naive approach of fitting the standard SAOM.

Social scientists have long been interested in elite cohesion in American society, recognizing its potential implications for democracy and governance. While empirical research has focused on corporate elites and, in particular, on cohesion derived from shared board memberships, cohesion among those in the highest positions in the American state and historical change in that cohesion have been little studied. Drawing on a novel dataset of the career histories of 2,221 people who were appointed to these elite positions between 1898 and 1998, I examine whether administrative elites, prior to their elite appointment, attended the same educational institutions or worked in the same agencies of the federal government at the same time. I find evidence of increasing elite cohesion during the twentieth century. Educational cohesion increases significantly in the three decades following the World War II and then declines slightly toward the end of the century. This increase goes hand in hand with a change from college to graduate education as the primary site generating educational cohesion. Federal government workplace cohesion increases markedly in the 1930s and 1940s and then remains high. As people are appointed to different organizations within the American state, their educational and workplace connections create inter-agency networks that, it is expected, facilitate mutual understanding and coordination and thus help integrate the American administrative state.

We investigate how the selection process of a leader affects team performance with respect to social learning. We use a laboratory experiment in which an incentivized guessing task is repeated in a star network with the leader at the center. Leader selection is either based on competence, on self-confidence, or made at random. In our setting, teams with random leaders do not underperform. They even outperform teams with leaders selected on self-confidence. Hence, self-confidence can be a dangerous proxy for competence of a leader. We show that it is the declaration of the selection procedure which makes non-random leaders overly influential. To investigate the opinion dynamics, we set up a horse race between several rational and naïve models of social learning. The prevalent conservatism in updating, together with the strong influence of the team leader, imply an information loss since the other team members’ knowledge is not sufficiently integrated.

Several studies have shown a relationship between the stocks of migrants and country-level investment in the home country; however the mechanism through which this relationship operates is still unexplored. We use a field experiment in which participants who are recent immigrants send information about risky decisions to others in their social network in their home country. The results demonstrate how this information influences decisions in the home country. We find that the advice given by family members and decisions made by friends significantly affects an individual’s risky decision-making.

This paper studies the effect of social relations on convergence to the efficient equilibrium in 2 × 2 coordination games from an experimental perspective. We employ a 2 × 2 factorial design in which we explore two different games with asymmetric payoffs and two matching protocols: “friends” versus “strangers”. In the first game, payoffs by the worse-off player are the same in the two equilibria, whereas in the second game, this player will receive lower payoffs in the efficient equilibrium. Surprisingly, the results show that “strangers” coordinate more frequently in the efficient equilibrium than “friends” in both games. Network measures such as in-degree, out-degree and betweenness are all positively correlated with playing the strategy which leads to the efficient outcome but clustering is not. In addition, ‘envy’ explains no convergence to the efficient outcome.

We assess the proposition that intergroup conflict (IGC) in non-human primates offers a useful comparison for studies of human IGC and its links to parochial altruism and prosociality. That is, for non-linguistic animals, social network integration and maternal influence promote juvenile engagement in IGC and can serve as the initial grounding for sociocultural processes that drive human cooperation. Using longitudinal data from three cohorts of non-adult vervet monkeys (Chlorocebus pygerythrus), we show that non-adults are sensitive to personal (age) and situational risk (participant numbers). The frequency and intensity of participation, although modulated by rank and temperament, both mirrors maternal participation and reflects non-adult centrality in the grooming network. The possibility of social induction is corroborated by the distribution of grooming during IGC, with non-adults being more likely to be groomed if they were female, higher-ranking and participants themselves. Mothers were more likely to groom younger offspring participants of either sex, whereas other adults targeted higher-ranking female participants. Although we caution against a facile alignment of these outcomes to human culturally mediated induction, there is merit in considering how the embodied act of participation and the resultant social give-and-take might serve as the basis for a unified comparative investigation of prosociality.

Astrobiology is often defined as the study of the origin, evolution, distribution and future of life on Earth and in the Universe and thought of as a discipline. In practice though, the delineation of astrobiology-related research and corresponding groups of researchers is far from straightforward. Here, we propose to apply text-mining methods to identify researcher communities depending on thematic similarities in their published works. After fitting a latent Dirichlet allocation topic model to the complete article corpus of three flagship journals in the field – Origins of Life and Evolution of Biospheres (1968–2020), Astrobiology (2001–2020), the International Journal of Astrobiology (2002–2020) – and computing author topic profiles, researcher communities are inferred from topic similarity networks to which community detection is applied. Such semantic social networks reveal, as we call them, ‘hidden communities of interest’ that gather researchers who publish on similar topics. The evolution of these communities is also mapped through time, bringing to light the significant shifts that the discipline underwent in the past 50 years.

The information deployment on social networks through word-of-mouth spreading by online users has contributed well to forming opinions, social groups, and connections. This process of information deployment is known as information diffusion. Its process and models play a significant role in social network analysis. Seeing this importance, the present paper focuses on the process, model, deployment, and applications of information diffusion analysis. First, this article discusses the background of the diffusion process, such as process, components, and models. Next, information deployment in social networks and their application have been discussed. A comparative analysis of literature corresponding to applications like influence maximization, link prediction, and community detection is presented. A brief description of performative evaluation metrics is illustrated. Current research challenges and the future direction of information diffusion analysis regarding social network applications have been discussed. In addition, some open problems of information diffusion for social network analysis are also presented.

Previous studies show how religious affiliation and activity often facilitate the integration of migrants and their descendants, strengthens their sense of belonging, and increases their acceptance in the host society. However, the characteristics of immigrants who benefit from the church’s help in the integration process remain largely unknown. This article addresses this gap in the literature and analyzes the ways in which the Neo-Protestant Church supports Romanian migrants in their integration in the US. We use primary data from an online survey conducted in September-November 2021 and semi-structured interviews conducted in 2022 with Romanian immigrants in the US. The results indicate that the church provides extensive help to people who are involved in religious organizations or associations, and to those who frequently attend religious services.

To date, most methods for direct blockmodeling of social network data have focused on the optimization of a single objective function. However, there are a variety of social network applications where it is advantageous to consider two or more objectives simultaneously. These applications can broadly be placed into two categories: (1) simultaneous optimization of multiple criteria for fitting a blockmodel based on a single network matrix and (2) simultaneous optimization of multiple criteria for fitting a blockmodel based on two or more network matrices, where the matrices being fit can take the form of multiple indicators for an underlying relationship, or multiple matrices for a set of objects measured at two or more different points in time. A multiobjective tabu search procedure is proposed for estimating the set of Pareto efficient blockmodels. This procedure is used in three examples that demonstrate possible applications of the multiobjective blockmodeling paradigm.

This paper generalizes the p* class of models for social network data to predict individual-level attributes from network ties. The p* model for social networks permits the modeling of social relationships in terms of particular local relational or network configurations. In this paper we present methods for modeling attribute measures in terms of network ties, and so construct p* models for the patterns of social influence within a network. Attribute variables are included in a directed dependence graph and the Hammersley-Clifford theorem is employed to derive probability models whose parameters can be estimated using maximum pseudo-likelihood. The models are compared to existing network effects models. They can be interpreted in terms of public or private social influence phenomena within groups. The models are illustrated by an empirical example involving a training course, with trainees' reactions to aspects of the course found to relate to those of their network partners.

Uniform sampling of binary matrices with fixed margins is known as a difficult problem. Two classes of algorithms to sample from a distribution not too different from the uniform are studied in the literature: importance sampling and Markov chain Monte Carlo (MCMC). Existing MCMC algorithms converge slowly, require a long burn-in period and yield highly dependent samples. Chen et al. developed an importance sampling algorithm that is highly efficient for relatively small tables. For larger but still moderate sized tables (300×30) Chen et al.’s algorithm is less efficient. This article develops a new MCMC algorithm that converges much faster than the existing ones and that is more efficient than Chen’s algorithm for large problems. Its stationary distribution is uniform. The algorithm is extended to the case of square matrices with fixed diagonal for applications in social network theory.