Social network extensions of Heider's balance theory have led to a plethora of adaptations, often inconsistent with Heider and each other. We present a general model that permits the description and testing of specific balance theoretic predictions as Heider had originally proposed them. We formulate balance statements as a comparison of two conditional probabilities of a tie: [Formula: see text], conditioned on 2-path relations [Formula: see text] vs. their counterfactual negation, [Formula: see text]. Here q, r and s, are indices that identify elements in a set of mutually exclusive and exhaustive relations in a multigraph (their sum produces a complete graph). This relaxes the dichotomous assumption of a signed graph. We identify neutral ties distinctive from negative and positive ties and convert the underlying signed graph into a restricted multigraph. The point-biserial correlations of relation q with the count of 2-paths (through relations r and s) describe the difference in conditional probabilities, or the prevalence for any stipulated balance configuration. In total 18 such unique balance correlations are identified for any trichotomous multigraph, although 27 theoretical statements exist. Two major advantages of balance correlations are: direct comparison between any two correlations over time, even if network sizes and densities differ; and the evaluation of specific balanced and unbalanced behaviors and predictions. We apply the model to a large data set consisting of friendly vs hostile relations between countries from 1816 to 2007. We find strong evidence for one of the balance statements, and virtually no evidence for unbalanced predictions. However, there is ample stable evidence that "neutral" ties are important in balancing the relations among nations. Results suggest that prevalence of balance-driven behavior varies over time. Indeed, other behaviors may prevail in certain eras.

Although who chooses to become a police officer and why they do so is pivotal for understanding policing, few studies explore recruit motivations. We help to fill this research void through analysis of open-ended narratives penned by police recruits during the academy in a large rust belt city explaining why they want to become police officers, supplemented with qualitative follow-up interviews conducted with randomly selected participants. Of the existing studies on police recruit motivation, nearly all use fixed-response surveys of researcher-selected answers. Despite our respondents being completely free to use their own words, their motivations demonstrate a striking similarity to the findings of previous literature. These stated motivations of altruism and community concern also stand in stark contrast to the public behaviors of police. We suggest this consistency in stated motivations and disconnect with public behavior evinces a publicly oriented vocabulary of motive in which police recruits are attempting to voice the “appropriate” reasons for joining the force.

The Koch Brothers have assembled a potent network of diverse allies to promote their libertarian agenda which includes, among other things, limited government, far less regulation of corporate entities engaged in polluting the environment and creating unsafe work environments. This network has organizations opposed to unions and granting minimum wages, providing adequate health care especially in the form of the social safety net. The key interests of these organization span a very wide range of ideological interests brought together with a primary intention of crippling democracy in the US. While we focus on environmental issues in this chapter, it is crucial also the recognize the wider network of organizations joined together into a large multi-purpose network designed to destroy democratic institutions designed to hold such extremists in check. This is especially the case concerning climate change denial.

There are many important societal issues facing the US and the world. There are also policy initiatives intended to deal with the problems raised by these issues. We couple some of the social issues and the policy initiatives to address them to the efforts of the Koch Brothers who created a powerful network of allies mobilized to support their libertarian ideas and ambitions. The relevant theoretical domain concerns the operation of social movements as the network of their allies forms a social movement. The mobilization includes obstructing many of the constructive policy initiatives discussed in the literature. This network has a diverse array of actors, each with their own core interests. A total of 443 Koch Brothers allies were identified. Their URLs were visited repeatedly to identify their core interests, for which a set of 47 keywords were established. A network for these keywords was constructed in which the links are the number of times they were shared by allies of the Koch Brothers. A variant of community detection was used to determine six communities of interests, all of which are very coherent. These communities are discussed in terms of the issues they raise for the US and the world. The implication for theory construction is to more fully understand the organization of actors opposing policy proposals for solving important societal problems and find effective ways for countering their destructive efforts.

Deterministic blockmodelling is a well-established clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering two-mode networks). Even though some of the historical foundations for certain types of blockmodelling stem from the psychological literature, applications of deterministic blockmodelling in psychological research are relatively rare. This scarcity is potentially attributable to three factors: a general unfamiliarity with relevant blockmodelling methods and applications; a lack of awareness of the value of partitioning network data for understanding group structures and processes; and the unavailability of such methods on software platforms familiar to most psychological researchers. To tackle the first two items, we provide a tutorial presenting a general framework for blockmodelling and describe two of the most important types of deterministic blockmodelling applications relevant to psychological research: structural balance partitioning and two-mode partitioning based on structural equivalence. To address the third problem, we developed a suite of software programs that are available as both Fortran executable files and compiled Fortran dynamic-link libraries that can be implemented in the R software system. We demonstrate these software programs using networks from the literature.

This volume was hard for me to evaluate.My conception of a handbook, on any topic, is that it needs to be comprehensive in its coverage.While I understand complete coverage of any field is exceedingly difficult, if not impossible, this handbook fails this criterion for reasons I will detail below.Even so, there is a large amount of useful information in this handbook despite its serious flaws.While assembling some great chapters, the editors did not draw attention to the many links between the chapters they had assembled.In terms of content, there are thirty-three chapters on a diverse set of social network analytic content.Due to space limitations, I cannot comment on them all.Some of the chapters are most informative and useful.While the opening chapter ("Introduction"), written by the editors, points towards divisions in the field, the chapter does little to deal with them.Of course, divisions in the field will exist.Fortunately, other chapters address this issue.Chapter 2 ("Network Basics," Light and Moody) introduces basic concepts in a reasonable fashion.But in doing so, the authors introduce the concept of boundary specification for networks, a most important concept.Yet they do not cite the authors who introduced this critical concern to the literature!Fortunately, this lapse was corrected in Chapters 7 and 8. Again, Chapter 2 provides little linkage between the content of the chapters in this handbook.I find this most unfortunate.Chapter 3 ("Theories of Social Networks," Fuhse) discusses the topic of theories in social networks.Clearly this is an important topic meriting extensive consideration.But the author attacks a manuscript in the literature that made a distinction between "network theory" and the "theory of networks."The distinction is very reasonable.But the author claims there is no theory here but only network mechanisms.Network mechanisms about networks are inherently theoretical.

The empirical focus is centered on the large social movement network created by the Koch Brothers to further their aims of transforming the US. The network was obtained by using the VOSON web crawler given a starting set of known allies of the Koch Brothers. This produced a large directed network with links between units. We propose using the idea of hubs and authorities as another way of considering the roles played by the units within this network. These roles may be more complex than has been realized. We have included an analysis of the core interests for the members of this network.

Deterministic blockmodelling is a well-established clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering two-mode networks). Even though some of the historical foundations for certain types of blockmodelling stem from the psychological literature, applications of deterministic blockmodelling in psychological research are relatively rare. This scarcity is potentially attributable to three factors: a general unfamiliarity with relevant blockmodelling methods and applications; a lack of awareness of the value of partitioning network data for understanding group structures and processes; and the unavailability of such methods on software platforms familiar to most psychological researchers. To tackle the first two items, we provide a tutorial presenting a general framework for blockmodelling and describe two of the most important types of deterministic blockmodelling applications relevant to psychological research: structural balance partitioning and two-mode partitioning based on structural equivalence. To address the third problem, we developed a suite of software programs that are available as both Fortran executable files and compiled Fortran dynamic-link libraries that can be implemented in the R software system. We demonstrate these software programs using networks from the literature.

We tackle two problems. One is understanding parts of the operation of the US Supreme Court. The other is a fundamental problem for network analysis. It is delineating the fundamental structures of networks. Even more important, within this second problem, is delineating changes of this network structure over time. We present a method for doing both for signed networks. The two problems are coupled closely as the data come from the completed years of the Roberts US Supreme Court, named after its Chief Justice, for the 2005 through 2018 terms. For the issues selected by the court for consideration in each term, the justices vote on the decision that will be issued by the court. These votes are either to support a decision or to dissent from it. These votes can be recorded into a signed 2-mode network for each term. While we examine these networks, our primary focus is on the 1-mode projection from the 2-mode network having the justices as units. Using signed relaxed structural balance blockmodeling, we establish the fundamental structure of the relations between justices for each of the terms. For 13 of the 14 terms considered, the criterion function for the blockmodels is zero. This structure changes in clear ways and shows that the conventional divide between conservative and liberal justices is overly simplistic. Of greater interest is identifying the structural roles of the court's justices.

This conclusion presents some closing thoughts on the concepts covered in the preceding chapters of this book and makes additional suggestions regarding potential future work. The book presents a wide variety of approaches and methods related to network clustering. It suggests that the institutional structure of science has a very large impact on the generation of scientific knowledge and the generation of scientific citation networks. The book advocates the optimization approach to the clustering problem. Using an appropriate criterion function, people can express their clustering goals, including the reduction of complexity, understanding network structures, and modeling networks. Together, optimization and the sought goals help define the nature of a “good” clustering. The book finishes by stressing two very general ideas. One is the importance of the exchange of ideas between different approaches with the goal of strengthening them. The second is the coupling of network processes and network structures to help readers understand both.

Recently, there have been significant advancements in the development of exact methods and metaheuristics for partitioning signed networks. The metaheuristic advancements have led commonly to adverse implications for multiple restart (multistart) relocation heuristics for these networks. Most notably, it has been reported that multistart relocation heuristics are not computationally feasible for large signed networks with thousands or tens of thousands of vertices. In this paper, we show that combining multistart relocation heuristics with tabu search or variable neighborhood search can rapidly produce partitions of the vertices of signed networks that are competitive with those obtained using existing metaheuristics.

Abstract Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL-median heuristic (TMKLMedH) seeks to optimize the same criterion as the RH for deterministic blockmodeling. The TMKLMedH runs much faster than the RH, so many more restarts of the TMKLMedH can be accomplished when the two methods are constrained to the same time limit. Three computational comparisons of RH and TMKLMedH were conducted using both synthetic and real-world networks. In all three comparisons, the superiority of TMKLMedH was unequivocal.

This work evaluates the finite sample behavior of ML estimators in network autocorrelation models, a class of auto-regressive models studying the network effect on a variable of interest. Through an extensive simulation study, we examine the conditions under which these estimators are normally distributed in the case of finite samples. The ML estimators of the autocorrelation parameter have a negative bias and a strongly asymmetric sampling distribution, especially for high values of the network effect size and the network density. In contrast, the estimator of the intercept is positively biased but with an asymmetric sampling distribution. Estimators of the other regression parameters are unbiased, with heavy tails in presence of non-normal errors. This occurs not only in randomly generated networks but also in well-established network structures.

Whenever major schisms between police and communities come to public attention, there are always passionate calls for an increased emphasis on - and improvement of - police training. This rhetoric is so common that police leaders joke that there is no societal problem so big that it can't be fixed by better police training. Still, professional socialization in law enforcement remains an important topic with a great deal of resources being devoted to developing initiatives and augmenting existing curricula. This training comes in many forms including learning the nuts and bolts of many legal processes and acquiring the practical skills for law enforcement. However, beyond this, there is a socialization process with multiple facets including the development of solidarity and trust among a cohort of recruits. We attempt to understand the basic mechanisms of network creation in police academies as the foundation of the socialization processes within them. By focusing on these network mechanisms underlying the establishment of the 'Thin Blue Line', we offer an understanding of the underlying social processes foundational for the transmission of police culture. In short, we think the recruit network structure functions as a vehicle for cultural transmission within police academies. (C) 2017 Elsevier B.V. All rights reserved.

Blockmodeling is viewed often as a data reduction method. However, this is a simplistic view of the class of methods designed to uncover social structures, identify subgroups, and reveal emergent roles. Worse, this view misses the richness of the method as a tool for uncovering novel human resource management (HRM) insights. Here, we provide a brief overview of some essentials of blockmodeling and discuss research questions that can be addressed using this approach in applied HRM settings. Finally, we offer an empirical example to illustrate blockmodeling and the types of information that can be gleaned from its implementation.

Although research collaboration has been studied extensively, we still lack understanding regarding the factors stimulating researchers to collaborate with different kinds of research partners including members of the same research center or group, researchers from the same organization, researchers from other academic and non-academic organizations as well as international partners. Here, we provide an explanation of the emergence of diverse collaborative ties. The theoretical framework used for understanding research collaboration couples scientific and technical human capital embodied in the individual with the social organization and cognitive characteristics of the research field. We analyze survey data collected from Slovenian scientists in four scientific disciplines: mathematics; physics; biotechnology; and sociology. The results show that while individual characteristics and resources are among the strongest predictors of collaboration, very different mechanisms underlie collaboration with different kinds of partners. International collaboration is particularly important for the researchers in small national science systems. Collaboration with colleagues from various domestic organizations presents a vehicle for resource mobilization. Within organizations collaboration reflects the elaborated division of labor in the laboratories and high level of competition between different research groups. These results hold practical implications for policymakers interested in promoting quality research.