Are my data good enough to create an archaeological network? What if I am missing some sites or contexts, or I have poor or variable quality information for some observations? Can I still apply network methods and models with incomplete and/or imperfect data, or should I not even attempt to use network methods? At this point, some of you may be asking yourselves questions along these lines. It is good to carefully consider potential data issues when conducting any archaeological analysis, but there are also some specific concerns revolving around sampling and data quality that deserve special attention when dealing with network data. In this chapter, we outline some of the most common issues you will encounter and further offer a generalized approach to identifying and assessing the potential impacts of sources of variation and uncertainty in network data through simulation and resampling.

Most of the past phenomena we study as archaeologists took place in physical space: individuals lived in homes and towns, and they moved through landscapes; they fought wars on battlefields and they exchanged goods from faraway places. Through our excavations, fieldwork, and literature studies we record spatial information such as the outlines of houses, the locations of sites, the slopes of terrain, or the distance between natural resources and settlements. Many relational phenomena are explicitly geographical, in that the medium of geographical space is an important aspect of the relationship itself. For example, road segments connect pairs of settlements that are close together, and lines of sight connect places from which observers can see features. Such phenomena could be quite straightforwardly represented as spatial networks since the nodes and edges are both explicitly embedded in physical space. But for other relational phenomena, space is more like a background feature that can be brought into analyses when relevant but does not feature prominently in the definition of either nodes or edges. For example, past food webs where species are connected through trophic flows or social networks where individuals are connected to their contacts both involve entities (nodes) and relationships (edges) that have spatial properties or attributes, but those spatial properties are not directly invoked in the definition of such networks. We refer to these as networks in space in that we could include spatial features into their network representations, but this is not explicitly included in their definition.

Networks are nothing more than a set of entities and the pairwise connections among them. This simple definition encompasses a tremendous amount of variation from communication systems like the internet to power grids to neurons in the brain to road systems and flights between airports to our own social networks defined through familial ties, acquaintance, or any manner of interaction one could imagine. Over the last 20 years or so, academic interest in networks and the complex properties of network systems has grown by leaps and bounds. This has been mirrored by a growing excitement by the public in general (see best-selling works including Barabási and Frangos 2014 and Watts 2004). It is not uncommon these days to see networks and network visuals used as explanatory tools in news stories or popular articles shared across social media (another kind of network) exploring the complicated connections among characters in television shows, books, or people and organizations involved in news stories. Everyone, it seems, is excited about networks and networks are everywhere.

The purpose of this chapter is to give you the basic lay of the land in the world of archaeological network research in order to provide context for the remainder of the book. As we saw in Chapter 1, although archaeologists have applied graph-theoretic and network analytic methods toward archaeological questions for more than 50 years, it is really only in the last 10 years or so that such approaches have become common. Archaeological network science is still quite a young subdiscipline and is constantly changing. There are likely to be some “growing pains” as we all figure out how to best adopt, adapt, and develop network methods appropriate for archaeological data and archaeological questions. This is perhaps not too different from where specializations like GIS were in archaeology 15–20 years ago (see Connolly and Lake 2006; Wheatley and Gillings 2002).

Functional magnetic resonance imaging (fMRI) was conceived in the early 1990s due to the coincidence of two advances: (1) MRI scanner technology able to support fast echo-planar imaging imaging techniques with the required temporal stability and (2) the scientific knowledge that differences in the magnetic susceptibility of blood may be associated with MRI signal changes based on alterations in blood oxygenation levels. These elements, together with the assumption that changes in blood oxygenation and volume would accompany changes in neural activity in the brain, motivated research groups around the world to develop fMRI.

In the mid-1980s a number of scientists and research bodies conceived the idea of determining the DNA sequence of the entire human genome. Initiated in 1990 and known as the Human Genome Project (HGP), this ambitious, publicly funded project relied on contributions from numerous international laboratories and remains the world’s largest collaborative biological-based project to date. The completion of the HGP thirteen years later in 2003 allowed scientists to view the human genome in its entirety for the first time [1]. It was thought that this would usher in a new age for biological research, allowing for a more comprehensive understanding of complex human diseases and phenotypes. While this was true to an extent, completion of this project led to a series of new, more complicated questions, as is often the case in research.

Epilepsy affects approximately 1% of the population [1]. Although generally treatable, up to 30% of patients do not achieve seizure freedom from anticonvulsive medication alone. Due to its relationship with cognitive abilities [2], quality of life [3], and the associated risk of premature death [4], drug-refractory epilepsy should be treated promptly. Temporal lobe epilepsy (TLE) associated with mesiotemporal sclerosis [5] and extra-temporal lobe epilepsy related to focal cortical dysplasia (FCD) [6] constitute the most common refractory epilepsy syndromes. Surgical resection of these lesions remains the treatment of choice [7], with success rates approaching 80% [8]. By allowing the detection of epileptogenic lesions and offering system-level mechanisms of the disease process, MRI has shifted the field from electro-clinical correlations toward a multidisciplinary approach.

Complex systems theory is a nebulous field whose overarching goal is to understand the dynamical behavior of systems consisting of many interconnected component parts. It has attracted widespread interest from many domains that study examples of such systems, including ecologists, sociologists, engineers, artificial intelligence researchers, condensed matter physicists, neuroscientists, and many others. The results of these collected, multi-disciplinary efforts have not been so much a comprehensive theory of Complex Systems (capital-C, capital-S), but rather a set of techniques, analogies, and attitudes toward problem solving that emphasize interactions and dynamics over individual components and their functions. The chapters are written in a complex adaptive systems frame and therefore it is useful to provide a provisional theoretical description of such systems. Following Holland [1], a generalizable description of complex adaptive systems is that they are collections of relatively simple agents that have the property that they can aggregate, so that collections of agents can form meta-agents (and meta-meta-agents etc.) with higher-order structure. These aggregates interact nonlinearly, so that the aggregate behavior of a collection of agents is qualitatively different from the behavior of the individual agents. The interactions among agents mediate flows of materials or information. Finally, the agents are typically diverse with distinct specialties that are optimized through adaptation to selective pressures in their environments.

The genetic underpinnings of epilepsy have come into much clearer focus over the past two decades. Advances in high-throughput molecular techniques have markedly improved our ability to identify potential therapeutic targets in epilepsy. Many of the monogenic effects identified through these methods have resulted in effective therapeutic targets for seizure amelioration [1,2,3]. Currently, around 200 definitively annotated epilepsy genes causing a range of seizure disorders and phenotypes have been identified [4]. Many more genes with putative associations with epilepsy pathways require further study [5]. The expansion of known genetic mechanisms and risk factors presents us with several benefits, including an increased pool of possible drug targets [6], genetic subtyping of seizure disorders [7], and the possibility for integrative analysis across different disorders [8,9]. However, the increasingly rich collection of genetic associations has also revealed the complexity of seizure disorders. Many mutations in different genes can converge on a similar clinical presentation [10], while different mutations in the same gene can have radically divergent outcomes [11,12]. Moreover, while robust data from twin and family studies demonstrate that common epilepsies are highly heritable [13,14], association studies have only detected risk factors that account for a small fraction of risk [15]. Thus, the data on epilepsy suggests a dichotomy. On one side, genetics is critical for describing etiology [16]. On the other side, using this information for prognosis or therapeutic development is limited by our current understanding of the complex genetic underpinnings of the disease and our analytic tools [10,17]. As a response to this complexity, researchers have started to shift toward complex systems approaches to genetics, which changes the focus from individual mutations to interactions among many mutations. The purpose of this chapter is to elaborate this ethos and present examples of this approach.