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An experiment in writing in public, one page at a time, by S. Graham, I. Milligan, & S. Weingart

Networks in Historical Research

1 Leave a comment on paragraph 1 1 Formal networks are mathematical instantiations of the idea that entities and connections between them exist in consort. They embody the idea that connectivity is key in understanding how the world works, both at an individual and a global scale. Graph theory, social network analysis, network science, and related fields have a history dating back to the early eighteenth century, cropping up in bursts several times since then. We are currently enjoying one such resurgence, not incidentally co-developing along with the popularity of the internet, a network backbone connecting much of the world to one system.

2 Leave a comment on paragraph 2 0 The use of formal network methods for historical research is much more recent, with only a few exceptions dating back beyond thirty years. Marten Düring has aggregated a thorough multilingual bibliography at http://historicalnetworkresearch.org for a list of specific instances. This chapter will go over a few examples of how historians have used networks, in what situations you might or might not want to use them, and the details of how networks work mathematically and technically. This first section covers the previous examples.

3 Leave a comment on paragraph 3 0 In the 1960s, Eugene Garfield created the “historiograph”, a technique to visualize the history of scientific fields using a network of citations or historical narratives laid out temporally from top to bottom.1 Garfield developed a method of creating historiographs algorithmically, and his contemporaries hoped the diagram would eventually be used frequently by historians. The idea was that historians could use these visuals to quickly get a grasp of the history of a discipline’s research trajectories, either for research purposes or as a quick summary in a publication.

4 Leave a comment on paragraph 4 0 Eugene Garfield's "Historiograph"Eugene Garfield’s “Historiograph”

5 Leave a comment on paragraph 5 0 A citation analysis by White and McCann2 looking at an eighteen century chemistry controversy took into account the hierarchical structure of scientific specialties. The authors began with an assumption that if two authors both contributed to a field, the less prominent author would always get cited alongside the more prominent author, while the more prominent author would frequently be cited alone. One scientist is linked to another if they tend to be subordinate to (only cited alongside of) that other author. The resulting networks, called entailograms, proved particularly useful in showing the solidification of a chemical “paradigm” over a period of 35 years. Lavoisier begins as a lesser figure in 1760, and eventually becomes the most prominent chemist by 1795; by that time, most chemists who were cited at all were cited alongside Lavoisier. Following the entailogram over time reveals conflict and eventual resolution.

6 Leave a comment on paragraph 6 0 White & McCann's "entailogram" of chemistry citations from 1772-1784.White & McCann’s “entailogram” of chemistry citations from 1772-1784.

7 Leave a comment on paragraph 7 0 White & McCann's "entailogram" of chemistry citations from 1791-1795.White & McCann’s “entailogram” of chemistry citations from 1791-1795.

8 Leave a comment on paragraph 8 0 Citation analysis, also called bibliometrics or scientometrics, is still a rapidly growing discipline, but despite the hopes of its founders and though many if its practitioners conduct historical research, historians rarely engage with the field.

9 Leave a comment on paragraph 9 4 Historical sociologists, anthropologists, economists, and other social scientists have been using formal network methods for some time, and tend to exchange ideas with historians more readily. One such early sociological work, by Peter Harris3, also employed citation analysis, but of a different sort. Harris analyzed citations among state supreme courts, looking at the interstate communication of precedent from 1870-1970. By exploring who relied on whom for legal precedent over the century, Harris showed that authority was originally centralized in a few Eastern courts, but slowly became more diffuse across a wide swath of the United States. Network approaches can be particularly useful at disentangling the balance of power, either in a single period or over time. A network, however, is only as useful as its data are relevant or complete; be careful when analyzing networks not to confuse power with a simple imbalance of available data.

10 Leave a comment on paragraph 10 1 In a study on nineteenth century women’s reform in New York, Rosenthal et al.4 reveal three distinct periods of reform activity through an analysis of organizational affiliations of 202 women reform leaders. These two hundred women were together members of over a thousand organizations, and the researchers linked two organizations together based on how many women belong to them both. The result was a network of organizations connected by the overlap in their member lists, and a clear view of the structure of women’s rights movements of the period, including which organizations were the most central. The study concludes, importantly, by comparing network-driven results to historians’ own hypotheses, comparing its strengths and weaknesses with theirs. For research on organizations, network analysis can provide insight on large-scale community structure that would normally take years of careful study to understand. As much as networks reveal communities, they also obscure more complex connections that exist outside of the immediate data being analyzed.

11 Leave a comment on paragraph 11 0 The study of correspondence and communication networks among historians dates back centuries, but its more formal analysis is much more recent. The Annales historian Robert Mandrou (1978) and the historian of science Robert A. Hatch (1998) both performed quantitative analyses of the Early Modern Republic of Letters, exploring the geographic and social diversity of scholars, but neither used formal network methods.

12 Leave a comment on paragraph 12 0 In a formal network study of Cicero’s correspondence, Alexander and Danowski5 make the point that large scale analyses allows the historian to question not whether something exists at all, but whether it exists frequently. In short, it allows the historian to abstract beyond individual instances to general trends. Their study looks in 280 letters written by Cicero; the network generated was not that of whom Cicero corresponded with, but of information generated from reading the letters themselves. Every time two people were mentioned as interacting with one another, a connection was made between them. Ultimately the authors derived 1,914 connections between 524 individuals. It was a representation of the social world as seen by Cicero. By categorizing all individuals into social roles, the authors were able to show that, contrary to earlier historians’ claims (but more in line with later historians), knights and senators occupied similar social/structural roles in Cicero’s time. This is an example of a paper which uses networks as quantitative support for a prevailing historical hypothesis regarding the structural position of a social group. Studies of this sort pave the way for more prominent network analyses; if the analysis corroborates the consensus, then it is more likely to be trustworthy in situations where there is not yet a consensus.

13 Leave a comment on paragraph 13 0 In what is now a classic study (perhaps the only study in this set relatively well-known beyond its home discipline), Padgett and Ansell6 used networks deftly and subtly to build a historical hypothesis about how the Medici family rose to power in Florence. The authors connected nearly a hundred 15th century Florentine elite families via nine types of relations, including family ties, economic partnerships, patronage relationships, and friendships. Their analyses reveals that, although the oligarch families were densely interconnected with one another, the Medici family – partially by design and partially through happy accident – managed to isolate the Florentine families from one another in order to act as the vital connective tissue between them. The Medici family harnessed the power of the economic, social, and political network to their advantage, creating structural holes and becoming the link between communities. Their place in the network made the family a swing vote in almost every situation, giving them a power that eventually gave rise to a three hundred year dynasty.

14 Leave a comment on paragraph 14 0 Padget & Ansell's network of marriages and economic relationships between Florentine families.Padget & Ansell’s network of marriages and economic relationships between Florentine families.

15 Leave a comment on paragraph 15 0 Padget & Ansell's network of friendships and political relationships between Florentine families.Padget & Ansell’s network of friendships and political relationships between Florentine families.

16 Leave a comment on paragraph 16 2 Before Facebook and MySpace, the first network of people to come to mind would probably kinship or genealogical networks with linkages between family members. Looking at a large town in southwest Germany in the early nineteenth century, Lipp7 explored whether and how the addition of an electoral system affected the system of kinship networks which previously guided the structure of power in the community. Surprisingly in an area to become known for its democratic reforms, Lipp showed that a half century of elections had not reduced the power of kinship in the community – in fact, kinship power only became stronger. Lipp also used the network to reveal the prominent actors of local political factions and how they connected individuals together. In this case, networks were the subject of study rather than used as evidence, in an effort to see the effects of political change on power structures.

17 Leave a comment on paragraph 17 2 Trade networks are particularly popular among economists, but have also had their share of historical studies. Using the records of nearly five thousand voyages taken by traders of the East India Company between 1601 and 1833, Erikson and Bearman8 show how a globalized economy formed out of ship captains seeking profit out of the malfeasance of private trade. Captains profited by using company resources to perform off-schedule trades in the East, inadvertently changing the market from a dyadic East-West route to an integrated and complex global system of trading. The authors used a network as evidence, in this case the 26,000 links between ports each time two were connected along a trading route. Over two hundred years, as more ports became connected to one another, the East India Company lost control in a swath of local port-to-port connections. The authors show that the moments at which private trade were at its peak were also critical moments in the creation of more complex trade routes. While network analysis is particularly powerful in these many-century longitudinal studies, they also must be taken with a grain of salt. Without at least second dataset of a different variety which is connected to the first, it is difficult to disentangle what effects were caused by the change in network structure, and what effects were merely external and changed both the network and the effect being measured.

18 Leave a comment on paragraph 18 0 Erikson & Bearman's geographic network of port-to-port shipping by the East India Company.Erikson & Bearman’s geographic network of port-to-port shipping by the East India Company.

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20 Leave a comment on paragraph 20 0 Folklorists have a long tradition of classifying folktales based on types, motifs, and various indices in order to make finding, relating, and situating those tales easier on the scholars balancing thousands upon thousands of tales. These schemes are often inadequate to represent the multidimensional nature of folktales, such as a tale which is classified as being about manor lords, but also happens to include ghosts and devils as well. Tangherlini and colleagues9 came up with a solution by situating a collection of nineteenth century Danish folktales in a network that tied tales to subjects, authors, places, keywords, and the original classification schemes, resulting in a network connecting 3,000 entities together by 50,000 ties and made them easily browsable in an online interface. The interface made it significantly easier for folklorists to find the tales they were looking for. It also aided in serendipitous discovery, allowing scholars to browse many dimensions of relatedness when they were looking at particular tales or people or places.

21 Leave a comment on paragraph 21 1 Lineage studies with networks are not limited to those of kinship. Sigrist and Widmer10 used a thousand eighteenth century botanists, tracing a network of between masters and disciples, to show how botany both grew autonomous from medical training and more territorial in character over a period of 130 years. The authors culled their group of botanists from various dictionaries and catalogues of scientific biography, and found by connecting masters to disciples, they saw botanists from different countries had very different training practices, and the number of botanists who traveled abroad to study decreased over time. The study juxtaposes a history of change in training practices and scientific communities against traditional large scientific narratives as a succession of discoveries and theories.

22 Leave a comment on paragraph 22 0 As is clear, historical network analysis can be used in a variety of situations and for a variety of reasons. The entities being connected can be articles, people, social groups, political parties, archaeological artefacts, stories, and cities; they can be connected by citations, friendships, people, affiliations, locations, keywords, and ship’s routes. The results of a network study can be used as an illustration, a research aid, evidence, a narrative, a classification scheme, and a tool for navigation or understanding.

23 Leave a comment on paragraph 23 2 The possibilities are many, but so too are the limitations. Networks are dangerous allies; their visualizations, called graphs, tend to be overused and little understood. Ben Fry, a leading voice in information visualization, aptly writes:

24 Leave a comment on paragraph 24 3 There is a tendency when using graphs to become smitten with one’s own data. Even though a graph of a few hundred nodes quickly becomes unreadable, it is often satisfying for the creator because the resulting figure is elegant and complex and may be subjectively beautiful, and the notion that the creator’s data is “complex” fits just fine with the creator’s own interpretation of it. Graphs have a tendency of making a data set look sophisticated and important, without having solved the problem of enlightening the viewer.

25 Leave a comment on paragraph 25 2 It is easy to become hypnotized by the complexity of a network, to succumb to the desire of connecting everything and, in-so-doing, learning nothing. The following chapter, beyond teaching the basics of what networks are and how to use them, will also cover some of the many situations where networks are completely inappropriate solutions to a problem. In the end, the best defense against over- or improperly- using a network is knowledge; if you know the ins and outs of networks, you can judge how best to use them in your research.

26 Leave a comment on paragraph 26 1 A sidebar section will take a look at archaeological network analysis, as what the archaeologists are doing should be of direct interest to the historians – especially the work of Carl Knappet, Tim Evans, Ray Rivers, Tom Brughmans, Barbara Mills, and many others. It is not coincidental that the Fortier Prize winners at DH2013, Evans and Jasnow, were working on a network approach to the Catalogue of Ships! (Their slideshow is here).

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References
  1. 28 Leave a comment on paragraph 28 1
  2. Garfield, E. (1973). Historiographs, librarianship, and the history of science. In C. H. Rawski (Ed.), Toward a theory of librarianship: papers in honor of Jesse Hauk Shera (pp. 380–402). Metuchon, NJ: Scarecrow Press. []
  3. White, D. R., & McCann, H. G. (1988). Cites and fights: material entailment analysis of the eighteenth-century chemical revolution. In Social structures: a network approach (pp. 380–400). Cambridge University Press. []
  4. Harris, P. (1982). Structural change in the communication of precedent among state supreme courts, 1870–1970. Social Networks, 4(3), 201–212. doi:10.1016/0378-8733(82)90021-1 []
  5. Rosenthal, N., Fingrutd, M., Ethier, M., Karant, R., & McDonald, D. (1985). Social Movements and Network Analysis: A Case Study of Nineteenth-Century Women’s Reform in New York State. American Journal of Sociology, 90(5), 1022–1054. []
  6. Alexander, M. C., & Danowski, J. A. (1990). Analysis of an ancient network: Personal communication and the study of social structure in a past society. Social Networks, 12(4), 313–335. doi:10.1016/0378-8733(90)90013-Y []
  7. Padgett, J. F., & Ansell, C. K. (1993). Robust Action and the Rise of the Medici, 1400-1434. American Journal of Sociology, 98(6), 1259–1319. []
  8. Lipp, C. (2005). Kinship Networks, Local Government, and Elections in a Town in Southwest Germany, 1800-1850. Journal of Family History, 30(4), 347–365. doi:10.1177/0363199005278726 []
  9. Erikson, E., & Bearman, P. (2006). Malfeasance and the Foundations for Global Trade: The Structure of English Trade in the East Indies, 1601–1833. American Journal of Sociology, 112(1), 195–230. doi:10.1086/502694 []
  10. Abello, J., Broadwell, P., & Tangherlini, T. R. (2012). Computational folkloristics. Communications of the ACM, 55(7), 60. doi:10.1145/2209249.2209267 []
  11. Sigrist, R., & Widmer, E. D. (2012). Training links and transmission of knowledge in 18th Century botany : a social network analysis. Redes: revista hispana para el análisis de redes sociales, 21(0), 347–387. []
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Source: http://www.themacroscope.org/?page_id=308