Genealogical narratives often include a strand of violence and physical effort for women, particularly through childbirth but also through exile, migration for marriage, and establishing an independent life, as the previous chapters show. This chapter explores genealogical transmission and its relationship to violence and women’s action in the context of administrative communication networks in the Middle English Athelston, in which the king kicks his wife, killing his heir, and sentences his pregnant sister to a trial by fire. Drawing on network theory, which emphasizes the “doers” and “doing” of a network, the chapter explores the alignment of the two royal heir-bearers with messengers, which positions the women as key transmitters, not unlike the Virgin Mary at the Annunciation, rather than as wives who simply carry their husbands’ children. In this model of transmission, the women influence succession not only through childbearing but also through royal petitioning, letter writing, and prayer.

For nineteenth-century British novels, illness did not exist just within the individual body; illness occurred at the level of communities. Responding to and building upon contemporary medicine’s focus on the social contexts of disease, novels of this era enlarge their plot structures to include more characters, more relationships, and more scopes of action than existed in novels before this century. In nineteenth-century novels, the collective experience of illness can be as intimate as the bonds between a sufferer and a caregiver or as diffuse as a global pandemic. In any case, illness revealed one’s embeddedness in larger structures of meaning. The formal characteristics of Victorian novels offer ways for critical medical humanities today to envision the social ties involved in the illness experience.

The Introduction argues that Faulkner discovered an epistemology for networked systems in the creation of his own imagined landscape. I present two major stages in which Faulkner’s discovery took place: (1) an earlier vision portraying how networks scale, circulate information, centralize, and produce potentially tyrannical paradigms of top-down vertical power; and (2) another view of dynamical networks that are constantly adapting to produce novel forms of movement and behavior. The Faulkner that this study evokes is at once the modernist developing a spatial narrative practice describing the emergence of complex social networks and the Romantic for whom the immanent life was paramount and even sacrosanct. That these two trajectories of inquiry and spiritual belief are not easily reconcilable gives philosophical and moral weight to the landscape and characters that Faulkner invented. They also provide a striking meditation on what it means for human beings to find themselves in systems so vast and ubiquitous that they can no longer remember what it was like to live outside them and, thus, to think outside of their ideological dicta.

In this chapter, we describe the main goal of the book, its organization, course outline, and suggestions for instructions and self-study. The textbook material is aimed for a one-semester undergraduate/graduate course for mathematics and computer science students. The course might also be recommended for students of physics, interested in networks and the evolution of large systems, as well as engineering students, specializing in telecommunication. Our textbook aims to give a gentle introduction to the mathematical foundations of random graphs and to build a platform to understand the nature of real-life networks. The text is divided into three parts and presents the basic elements of the theory of random graphs and networks. To help the reader navigate through the text, we have decided to start with describing in the preliminary part (Part I) the main technical tools used throughout the text. Part II of the text is devoted to the classic Erdős–Rényi–Gilbert uniform and binomial random graphs. Part III concentrates on generalizations of the Erdős–Rényi–Gilbert models of random graphs whose features better reflect some characteristic properties of real-world networks.

At the mesoscale, the fluctuating phenomena are described using the theory of stochastic processes. Depending on the random variables, different stochastic processes can be defined. The properties of stationarity, reversibility, and Markovianity are defined and discussed. The classes of discrete- and continuous-state Markov processes are presented including their master equation, their spectral theory, and their reversibility condition. For discrete-state Markov processes, the entropy production is deduced and the network theory is developed, allowing us to obtain the affinities on the basis of the Hill–Schnakenberg cycle decomposition. Continuous-state Markov processes are described by their master equation, as well as stochastic differential equations. The spectral theory is also considered in the weak-noise limit. Furthermore, Langevin stochastic processes are presented in particular for Brownian motion and their deduction is carried out from the underlying microscopic dynamics.

The concept of a “focus of activity” was developed to describe the nature of non-network social structures that often underly the formation of intersecting clusters of ties in large-scale social networks. The primary purpose of the “focus theory” was to encourage network analysts to consider the impact of those underlying structures before attributing too much of the structure and consequences of social networks to psychological processes, individual choices, or self-contained network processes. We have been pleased to see that network researchers have increasingly considered the impacts of these non-network social structures. Nevertheless, we take this opportunity to clarify and extend the focus theory in ways that may make it more applicable and useful for more purposes. We show how the focus theory can be reconciled with some influential network theories that may initially appear inconsistent with it. We discuss how the focused organization facilitates indirect connections across large-scale social networks; and, we explicitly extend the focus theory by considering additional processes causing both the loss of old ties and the accumulation of new ties within clusters over time.

Chapter 1 lays out the cornerstones of our argument. We highlight how power is multidimensional and is related to network position. We review several works on field theory, contest arenas, and social spaces to highlight how analysts can theorize political action in multimodal settings. Then we explain why communities are a key concept for theory and research in political networks: how they can be identified, how they are created, and what effects they have on individual-, community-, and systemic-level outcomes. Last, we show that while some researchers have studied multimodal social networks (particularly 2-mode networks), few have conducted systematic treatments of multimodal political networks.

Recently, there has been increased focus on sub-threshold stages of mental disorders, with attempts to model and predict progression to full-threshold disorder. Given this considerable research attention and clinical significance, it is timely to analyse the assumptions of theoretical models in the field. Research into predicting onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (i.e., a "snapshot" of clinical state and other risk markers) and may benefit from taking dynamic changes into account. Cross-disciplinary approaches to complex system structures and changes, such as dynamical systems theory, network theory, instability mechanisms, chaos theory and catastrophe theory, offer potent models that can be applied to emergence (or decline) of psychopathology, including psychosis prediction and transdiagnostic symptom emergence. Staging provides a useful framework to research dynamic prediction in psychiatry. Psychiatric research may benefit from approaching psychopathology as a system rather than a category, identifying dynamics of system change (e.g., abrupt/gradual psychosis onset), factors to which these systems are most sensitive (e.g., interpersonal dynamics, neurochemical change), and individual variability in system architecture and change. The next generation of prediction studies may more accurately model the highly dynamic nature of psychopathology and system change, with treatment implications, such as introducing a means of identifying critical risk periods for mental state deterioration.

This chapter describes our changing views on psychopathology and our attempts to model it. In particular, it argues how, for a significant step forward in mental health research, we should acknowledge the complex nature of mental illness. We introduce an exciting new paradigm in the field of psychopathology: the network paradigm. We discuss the basics of network theory, its application to psychopathology and some future directions of where this avenue may take us.

A fundamental aim of diagnosis is to guide treatment planning and predict illness course. Yet for too long psychiatric diagnosis, grounded on traditional silo-based approaches, has lacked clinical utility. This chapter explores the purpose of diagnosis and classification as well as the inability to validate diagnosis in psychiatry. It is proposed that new testable models are needed to improve the utility of diagnosis and support more personalised and sequential treatment selection. A number of new approaches have been put forward, including hierarchical and network-based methods, however at present, these offer limited value in guiding treatment selection. Clinical staging offers a viable solution. Clinical staging in psychiatry recognises that mental disorders are not static and discretely defined entities, but rather they are syndromes that overlap and develop in stages. The model ensures that interventions are proportional to both need and the risk of progressing to later stages and more established syndromes, which are likely to be comorbid, persistent, recurrent and disabling. Ultimately, it advocates a transdiagnostic approach to intervention, with a pre-emptive focus, that is based on risk-benefit considerations and patient needs. Clinical staging also provides a framework in which underlying biological mechanisms can be linked to each stage, to build a personalised and pre-emptive psychiatry.