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.

This paper generalizes the p* model for dichotomous social network data (Wasserman & Pattison, 1996) to the polytomous case. The generalization is achieved by transforming valued social networks into three-way binary arrays. This data transformation requires a modification of the Hammersley-Clifford theorem that underpins the p* class of models. We demonstrate that, provided that certain (non-observed) data patterns are excluded from consideration, a suitable version of the theorem can be developed. We also show that the approach amounts to a model for multiple logits derived from a pseudo-likelihood function. Estimation within this model is analogous to the separate fitting of multinomial baseline logits, except that the Hammersley-Clifford theorem requires the equating of certain parameters across logits. The paper describes how to convert a valued network into a data array suitable for fitting the model and provides some illustrative empirical examples.

Social network data represent interactions and relationships among groups of individuals. One aspect of social interaction is social influence, the idea that beliefs or behaviors change as a result of one’s social network. The purpose of this article is to introduce a new model for social influence, the latent space model for influence, which employs latent space positions so that individuals are affected most by those who are “closest” to them in the latent space. We describe this model along with some of the contexts in which it can be used and explore the operating characteristics using a series of simulation studies. We conclude with an example of teacher advice-seeking networks to show that changes in beliefs about teaching mathematics may be attributed to network influence.

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semi-partialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.

In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation–maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore the French financial elites based on their social networks and their career, political views and social status. We observe a division in the social circles of the French elites in accordance with the differences in their attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package “jlsm” is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf.

The dissemination and implementation (D&I) of evidence at the community level is critical to improve health and advance health equity. Social networks are considered essential to D&I efforts, but there lacks clarity regarding how best to study and leverage networks. We examined networks in community-level D&I frameworks to characterize the range of network actors, activities, and change approaches. We conducted a narrative review of 66 frameworks. Among frameworks that explicitly addressed networks – that is, elaborated on network characteristics, structure, and/or activities – we extracted and synthesized network concepts using descriptive statistics and narrative summaries. A total of 24 (36%) frameworks explicitly addressed networks. Commonly included actors were implementers, adopters/decision-makers, innovation developers, implementation support professionals, and innovation recipients. Network activities included the exchange of resources, knowledge, trust, and norms. Most network-explicit frameworks characterized ties within and across organizations and considered element(s) of network structure – for example, size, centrality, and density. The most common network change strategy was identifying individuals to champion D&I efforts. We discuss opportunities to expand network inquiry in D&I science, including understanding networks as implementation determinants, leveraging network change approaches as implementation strategies, and exploring network change as an implementation outcome.

Cooperative behavior constitutes a key aspect of human society and non-human animal systems, but explaining how cooperation evolves represents a major scientific challenge. It is now well established that social network structure plays a central role for the viability of cooperation. However, not much is known about the importance of the positions of cooperators in the networks for the evolution of cooperation. Here, we investigate how the spread of cooperation is affected by correlations between cooperativeness and individual social connectedness (such that cooperators occupy well-connected network positions). Using simulation models, we find that these correlations enhance cooperation in standard scale-free networks but not in standard Poisson networks. In contrast, when degree assortativity is increased such that individuals cluster with others of similar social connectedness, we find that Poisson networks can maintain high levels of cooperation, which can even exceed those of scale-free networks. We show that this is due to dynamics where bridge areas between social clusters act as barriers to the spread of defection. We also find that this positive effect on cooperation is sensitive to the presence of Trojan horses (defectors placed within cooperator clusters), which allow defection to invade. The results provide new knowledge about the conditions under which cooperation may evolve, and are also relevant to consider in regard to the design of cooperation studies.

Debate regarding the continuity of Cypriot political forms from the Late Bronze Age to the Cypro-Archaic is persistent, resulting in a scholarly divide with few signs of resolution. This article reviews the historiography of political forms proposed for Cyprus as the essential context for this debate. It considers several major themes that emerge from the debate: the use of anthropological models for state formation, regionalism, social networks, and the nature of spatial power. The author views the debate as centred on two equally valid motivations: using related social science theory to enhance archaeological explanation and emphasizing Cypriot autonomy. These motivations need not be set in opposition but, together, illustrate the island's unique history and provide the basis for vibrant scholarship.

The moderation of user-generated content on online platforms remains a key solution to protecting people online, but also remains a perpetual challenge as the appropriateness of content moderation guidelines depends on the online community that they aim to govern. This challenge affects marginalized groups in particular, as they more frequently experience online abuse but also end up falsely being the target of content-moderation guidelines. While there have been calls for democratic, community-moderation, there has so far been little research into how to implement such approaches. Here, we present the co-creation of content moderation strategies with the users of an online platform to address some of these challenges. Within the context of AutSPACEs—an online citizen science platform that aims to allow autistic people to share their own sensory processing experiences publicly—we used a community-based and participatory approach to co-design a content moderation solution that would fit the preferences, priorities, and needs of its autistic user community. We outline how this approach helped us discover context-specific moderation dilemmas around participant safety and well-being and how we addressed those. These trade-offs have resulted in a moderation design that differs from more general social networks in aspects such as how to contribute, when to moderate, and what to moderate. While these dilemmas, processes, and solutions are specific to the context of AutSPACEs, we highlight how the co-design approach itself could be applied and useful for other communities to uncover challenges and help other online spaces to embed safety and empowerment.

This article focuses on the involvement of Viennese elites in wide-reaching political conflicts around 1400. Central European princes often held positions as city lords, which resulted in ambivalent relations between them and urban elites, as well as with their kin residing in the countryside. Setting aside grand categories of institutional history in favor of the interactions and relations of concrete actors, their social networks, and their involvement in shaping politics, the article follows six urban actors through a major conflict that involved the city lords, urban authorities, and individual actors and eventually resulted in the beheading of three of them. The article adopts a prosopographical approach to find out more about patterns of social costs and benefits in these conflicts. It argues that considering polyvalent and relational dimensions of belonging can help us better understand constellations of conflict and alliance and the modes and mechanisms of late medieval politics. It eventually establishes the boundaries of social network approaches when it comes to assessing individual motives and their alleged resonance in contemporary narratives of community.