The Soviet film industry, like any other institution, was made up of networks of people who worked together, or who worked with people who had worked together. In this article, I analyse some of those connections using digital social network analysis (SNA). The history of Soviet film has usually focused on the role of state censorship and ideology in shaping artistic choices, often focusing on individual directors. More recently, though, focus has shifted somewhat to include studies of the institutions and technologies of film production.1 This extensive literature generally shows that, during its first decades, the history of Soviet film production was a history of almost continual shifts in priorities, funding possibilities, political requirements, technologies, and industry leadership. As the political system centralised under Stalin, so too did the film industry, all of which had profound effects on the kinds of films that were made and how they were made. Social network analysis of connections between the people involved in film making gives us a new perspective on the impact of these shifting conditions on the ways films were made. By looking at a few specific measures of work connections among a very large number of people, we gain new insights into the social structures of Soviet film production and raise important new questions for future research.
One of the main functions of SNA is to measure what is called ‘centrality’ by mapping the connections between individuals and identifying points in a network that have high degrees of connection with others. Centrality in a social network, however, differs somewhat from centrality in a political system: while social networks have dynamics that intersect with politics, they have dynamics of their own that develop independently. My goal in this article is to show the ways political centralisation and network dynamics intersect in the early Soviet film industry.
Social network analysis generates new insights and questions by measuring connections between individuals. People (like animals, trees, and fungi), are social beings: we thrive, we fail, we communicate, and we grow through our connections with others. Those connections can take many forms – kin, friend, acquaintance, enemy, colleague, assistant, or boss, political ruler or media celebrity, pets or plants – and they can be of varying intensity and significance – strong, weak, harmonious, contested, obligatory, or voluntary. Together these crisscrossing social connections between individuals create networks, and those social networks have been the center of the study of who we are, alone and together, at least since people started writing down their thoughts.
Analysis of these networks is not new, but the rise of professional sociology in the nineteenth and early twentieth centuries saw the development of efforts to study networks empirically and mathematically. Early sociological network analysis departed from other forms of social research in focusing on the structures of connection linking individuals rather than on the behavior or beliefs of individuals or groups (Freeman 2004: 2). For example, SNA was used by sociologists to study the distribution of friendship and kin relationships by studying the number of people who knew each other or were related to each other in a specified location and the relative importance of competing trading centers in a given region by looking at the number of journeys taken from export centers to import centers (Freeman 2004). These connections, which seem quite simple, offer perspectives on such important aspects of social interaction as information flows, the relative ability to influence others, the distribution of groups into discrete communities, and the degree of integration within groups. As a method for studying human relationships, the measurement of specific connections between a discrete number of people or places made social network analysis conducive to mathematical analysis and then to computerisation (Kadushin 2012, Watts 2003). The advent of personal computers, access to large sets of data via the internet, and programs that simplified data processing make the tools of social network analysis widely available.
Today social network analysis has applications in all fields of the humanities and social sciences as well as in health care, urban planning, marketing, human resource management, and other fields. In health care, for example, contact tracing, so essential to containing the spread of infectious diseases, is based on the fundamentals of social network analysis and SNA programs are helping health professionals track and maintain records they collect about the potential spread of disease from one person to another (Chen 2011, Valente 2017). In the humanities, one of the most well-known SNA projects is “Six Degrees of Francis Bacon,” which maps approximately 88 million social connections between individuals in early modern Britain, with the statesman and philosopher, Francis Bacon, at the center. The title is a reference to the theory that all the people on earth are linked in six or fewer “friend of a friend” connections (also known as degrees of separation) to each other. This theory, originally proposed by Frigyes Karinthy in 1929, led to the popular 1990s game, “Six Degrees of Kevin Bacon,” in which people would try to establish the number of connections between any Hollywood actor and the prolific Kevin Bacon. Anyone who acted in a film with Bacon receives a score of ‘one’ (or one degree of separation); anyone who never acted with Bacon but acted in a film with someone who did act with Bacon received a score ‘two’ and so on (Watts 2003: 93, Kadushin 2012: 108-34). In scholarly film studies, such SNA measures seem to be used most often to try to predict film success by measuring the person-to-person dissemination of media attention or by studying patterns of interaction among characters, rather than to study working connections in film production history as I propose here.2
Since the beginning of social network analysis, data visualisation has been a key component. According to Linton Freeman, a sociologist and historian of SNA, it was Jacob Moreno, one of the pioneers in the field, who invented the basic visual form that all SNA studies still take, including the 88 million connections in “Six Degrees of Francis Bacon.” Moreno began by thinking about how to identify a single relationship that might connect two individuals in a way that could then be scaled up to measure large numbers of the same kind of connection. He diagrammed these connections using points to represent social actors and lines to represent their connection (Freeman 2000). Moreno viewed these graphs as more than simple representations or solely methods of presenting research. They were “first of all a method of exploration” (Freeman 2000). Moreno understood that visualisation made abstract concepts more concrete, which allowed researchers to develop an understanding of ideas or see patterns in the data that might be more difficult to extract from a large data set than a visual graph. As I will demonstrate below, I found this to be the case in my own research: patterns in the data that I was able to see first in the visualisation raised key questions that I would then test in the corresponding data and historical sources.
In general, the shift from qualitative humanities research to big data quantitative research not only involves new methods and technologies, it requires a profound shift in modes of thinking. To do SNA, you have to learn to think like a computer. Computers, of course, do not ‘think,’ but they process information in ways that are distinctly different from the ways scholars in the humanities are accustomed to thinking. Getting a PhD in history, for example, requires us to learn some new skills, but they’re mostly refinements of skills we’ve been practicing since we started going to school. Digital history, on the other hand, demands a different way of thinking. As historians (or humanists more generally), we often think about people in contexts that involve multiple, overlapping interactions of various kinds: social, political, cultural, and economic, to put it in the simplest terms. The quantifiable interactions that computer programs (and network analysts and computer scientists) usually work with must be discrete units, broken down to small, clearly definable attributes. As one network specialist, Scott Weingart, puts it: “Humanistic data are almost by definition uncertain, open to interpretation, flexible and not easily definable. Node types (nodes are fundamental units of networks) are concrete; your object either is or is not a book. Every book-type thing shares certain unchanging characteristics” (Weingardt 2011). For dealing with large numbers of films and large numbers of people, turning qualitative texts into quantitative data – like who worked with whom or how often individual words are spoken – is worthwhile because the large scale of those phenomena combined with computational speed with which computers can process them create new kinds of knowledge and raise new questions. These data points may look simple (or simplistic) to humanists accustomed to thinking about art works or production processes in all their complexity, but it’s not simple to think effectively in these terms. To translate familiar kinds of nuanced, overlapping contexts and interactions into meaningful digital structures that yield new insights requires new modes of thinking. On the other hand, Miriam Posner has argued that while humanists often need help with data modeling in order to organise large bodies of information in ways that make a computer produce something useful, it is equally true that computational digital scholars need help from humanists to make a computer produce something meaningful (Posner 2015).
The present study builds on a preliminary social network analysis done in 2013 by Seth Bernstein, who examined social networks in the Soviet film industry by making a database of the artists who worked on films between 1918–1991 (Bernstein 2013). This database was derived from the lists on the Russian website kino-teatr.ru, which include everyone listed as cast and crew on individual films: directors, actors, producers, cinematographers, and others. Bernstein was also interested in measuring the connectedness of people who worked in Soviet film: who worked with whom? He showed several interesting things about the connectedness of these film workers. He showed, first, that the Soviet film industry was densely connected. Almost everyone in his data set was connected in six steps or less, but the average degree of separation between people is actually much lower than six—2.76—and the majority of Bernstein’s film people are connected by 3 or fewer degrees (meaning that the majority of people who worked on films between 1918 and 1991 were connected to each other through only two intermediaries). Bernstein also employed what are called “weighted” measurements that take into account not only if people worked together, but how often people worked together; emphasis on those repetitive ties indicates an even more densely connected film industry overall. Bernstein showed that the most highly connected people who also had higher rates of repeated ties were neither acclaimed directors nor beloved actors, but the journeymen (men, specifically: women rarely rose to high connectivity). The most highly connected people were those who worked a lot over a long period of time and in a variety of roles (director, actor, writer, for example), with people who themselves were highly connected. As Bernstein put it: “What this calculation of network centrality measures is not necessarily fame but rather position… a different kind of influence (maybe banal) lost with qualitative sources. Those sources tend to focus on the brightest figures but don't register those ubiquitous people who stood out less” (Bernstein 2013). This conclusion indicates that centrality in a social network is different from centrality in a political system or popularity in a social group.
In his study of social connectedness, Duncan J. Watts writes that the study of networks shows us that fame and high status, traditional markers of centrality, do not always drive events or control institutions. “In a multitude of systems from economics to biology, events are driven not by any preexisting center but by the interaction of equals” (Watts 2003: 53). We might consider the range between economics and biology to be rather narrow, but this insight drives many productive questions about networks, including my own. What kinds of networks do we find if we think about centrality, or the condition of being highly connected mathematically as equally important as fame? How important is it to be highly connected, that is, to work with the greatest number of people, and what can that tell us about social structures?
My project looks into the connections Bernstein studied in more detail in order to break into the density that he found and examine the significance of some of those well-positioned people, the specific people they were connected to, and the “interactions of equals” that took place outside the centers of fame and authority. I also studied a shorter chronological period than Bernstein, 1918–1953, in order to measure changes over time. I limited my study to connections between directors and actors, because these are the only categories that kino-teatr.ru lists systematically for every film made during this period. Directors and actors hardly constitute the total group of people involved in making a single film, but they are the public face of filmmaking, and were often in high demand by studios, which makes their connections significant in their own right. Although limiting myself to directors and actors means that I will not be examining Soviet film production in its entirety, this introductory study of a limited number of connections brings out unexpected and revealing connections between people.
My first database includes film directors and actors who worked together on a film at some point during the whole period of the study, 1918–1953.3 We then broke that database down to make individual databases for the periods that roughly correspond to political and film industry shifts: 1918–1923, 1924–1929, 1930–1935, 1936–1940, 1941–1946, and 1947–1953 (the year Stalin died).4 The first question is: How many people are we studying? People in social networks are referred to as “nodes” and the connections between them are referred to as “edges.” I used the SNA program Gephi (gephi.org) to analyse and visualise the data. Gephi showed that there were a total of 4810 people or nodes with 78,056 connections or edges among them.
Gephi analyses those edges (or connections or degrees of separation) in a number of useful ways. At the simplest level, it calculates the number of connections between any two nodes or people and it weights those degrees for repeated connections (as Bernstein did). As the reader can see in figure 2, the average number of steps between nodes was just a bit higher, meaning just a bit longer, for the 1918–53 period than Bernstein found for the whole Soviet period 1918–91: just over 3. That means that actors and directors who worked on films in the period 1918–1953 were connected by more than two intermediaries for a rating of more than three rather than a rating of 2.76 in the period 1918–1991. I’ll come back to the reasons for this disparity below.
Gephi can also show specific kinds of weighted connections, that is connections between individuals that show rates of connectivity. The networks produced by these weighted connections can show individuals who are especially highly connected in various ways or what SNA scholars call their “centrality.” The type of connection I’m most interested here in Eigenvector centrality, which measures the number of connections between a node or person with people who are themselves highly connected. In addition to the number of connections any one person has or the length of the path between individuals – the number of steps it takes to link people – Gephi can tell us not just who has the most connections, but who has the most connections that matter. This kind of centrality does not necessarily point to the most famous or powerful actors and directors. By so doing it indicates a different definition of “centrality” (Kadushin 2012, Freeman 1979).
Second, Gephi does a very useful division of nodes into what it calls “communities.” It can count up all the connections between individuals and indicate relative ratios of connectivity, which results in distinguishing groups of nodes with closer connections to each other than they are to nodes in the network as a whole. Some of these groups or communities are even more closely, densely connected than other communities and Gephi analyses these communities for degrees of that density, which it calls “modularity.” Modularity compares degrees of community connectivity (Meeks 2011). Gephi also turns these measures into more intuitive data visualisations that make these abstract calculations more concrete and comprehensible.
Figure 3 is a Gephi-generated data visualisation that graphs the connections between directors and actors for the whole period, 1918–1953, colourised to indicate communities (that is groups of individuals more closely connected to each other than to others outside their community) and with node sizes based on their Eigenvector score (that is people who know people who know a lot of well-connected people are represented as larger circles). Gephi uses gravitational algorithms to make these graphs: nodes are driven out from a common starting point based on their size (or gravitational weight) but at the same time they are driven towards nodes they are strongly connected to.
The first thing to notice is that even though this network is clearly very interconnected (the solid coloured areas are made up of individual node dots), it is just as clearly divided into communities. Not surprisingly these communities partially break down in rough geographic terms. My analysis of the content of communities is based entirely on surnames associated with different regions and ethnicities. The orange and pink nodes at the bottom are predominantly Georgian and Armenian names.
The blue, green, and pink are overwhelmingly filled with nodes with Russian names. Names in blue are mostly people associated with the Leningrad film studio, Lenfilm; and the pink and green, with Mosfilm, the Moscow film studio (I haven’t discovered the difference between pink and green communities); the black in figure 3 is largely Ukrainian.
Each community is both clearly distinct but very much tied to the whole. Even after Mikhail Gelovani, who became well-known for playing Stalin in a number of films, and the prominent Georgian director, Mikhail Chiaureli, are integrated into the Soviet film industry, their connections measured in the aggregate for this period identify them as more connected to their geographical-ethnic community. The strong connectivity in geographic terms, I suspect is the answer to the question I raised earlier. This is why individuals are less closely connected as a whole in the 1918–1953 period than they are in the 1918–1991 period as Bernstein discovered. And this tells us something interesting about this period: in this earlier period the regional film industries were less integrated into the whole and individuals less connected with filmmakers in other regions. One of the things I discovered is how that changes. But first, what about those nodes/people?
Who rises to the top with high scores for Eigenvector centrality, that is they are connected to people who are themselves highly connected (see Figs. 5 and 6)? Vladimir Gardin was a prominent prerevolutionary actor, director, and, later, screenwriter; he was also one of the founders of the first Soviet film school, participating altogether in over 100 films. A notable number of the figures with the next size dots are associated with one of the most famous Soviet directors, Sergei Eisenstein: Nikolai Cherkasov, Oleg Zhakov, Aleksei Abrikosov, and Mikhail Zharov; Amvrosii Buchma in Ukraine, all had independent careers, but they also all played roles in Eisenstein’s Ivan Groznyi / Ivan the Terrible (1944–46/58, USSR). Maksim Shtraukh was a prominent stage and screen actor and Eisenstein’s childhood friend. Eisenstein, himself, though, has a low Eigenvector despite being arguably the most important individual in the early film industry, and the Artistic Director of Mosfilm after that position was introduced in 1941. Many people, therefore, with close connections to Eisenstein have high Eigenvector scores. The person with the highest Eigenvector score for 1918–53, the Kevin Bacon of the Soviet screen (or, in fact, the Christopher Lee, who was the most highly connected actor in Hollywood of the 562,600 actors listed in Wikipedia at the time of calculation) is Vladimir Ural’skii. One has to have lived in the Soviet Union during this period and known a lot about Soviet actors to know the name Vladimir Ural’skii, but he had a prolific career.
Born Vladimir Popov in 1887 in Orenburg, he went to work at age 8 in a bakery but, as his official biography put it, he did not find his passion there. Аt 22 in 1909 he joined a theater where he achieved some acclaim. Just before the First World War, he was accepted to study at MKhAT, the acclaimed Moscow Art Theater, but in 1913 was exiled to Helsinki for political untrustworthiness. After the First World War, the Russian Revolution, and Civil War, he returned to Moscow in 1923 and got his first acting role in what would be one of the most important films of the silent period, Eisenstein’s first feature film, Strike, where he played a worker (kino-teatr.ru). He worked steadily during this entire period, acting in 136 films starting with Aelita (Yakov Protazanov, 1924, USSR) and ending with Zelenyе ogni / Green Lights (Sploshnov and Shul’man, 1955, USSR).
He worked on prestige films with great and famous directors, including many of the classics: he was in a worker in Eisenstein’s Stachka / Strike, (1924, USSR), a sailor in Bronenosets Potemkin / Battleship Potemkin (1925, USSR) and a priest in Ivan the Terrible (1944–46/58). In addition to these, he was in films directed by Yakov Protozanov, Vsevolvod Pudovkin, Oleksandr Dovzhenko, Grigorii Kozintsev and Leonid Trauberg, Boris Barnet, Ivan Pyr’iev, Lev Kuleshov, Mikhail Romm, Aleksandr Zarkhi and Iosif Kheifits, Fridrikh Ermler, and Olga Preobrazhenskaia. He was in Mikhail Zharov’s first film as a director, and he was in Leonid Lukov’s Bol’shaia zhizn’ / Great Life (1946, USSR), which Stalin singled out for criticism in 1946 along with Ivan Groznyi Part II and Pudovkin’s Admiral Nakhimov (1947, USSR). After the war, he was in films directed by Iulii Raizman and Sergei Gerasimov; he was in Pyr’ev’s Kubanskie kazaki / Cossacks of Kuban, and he was in Stalingradskaia bitva / The Battle of Stalingrad (Vladimir Petrov, 1948–49, USSR) and Revizor / The Inspector General (Vladimir Petrov, 1952, USSR). But of the 130 films he appears in, he played named roles in only 36 of them (28%).
Usually he played bit parts: a worker, a committee member, a partisan, a soldier, a driver, a student, a bureaucrat, an old man, a couple generals, a merchant, a millionaire, and a hooligan. He also did voices for animated films (not recorded in the database). I will come back to further discuss his career after we look at the breakdown of these graphs into separate periods.
What happens to degrees of connectivity and community when we look at changes over time.
The first period I examined, 1918–1923, was a time when early Soviet film production was not at all centralised. Russian production was in shambles after the revolution and during the civil war, but this did not stop individuals from developing production outside of Russia. In 1920 Ivan Perestiani, who had been a successful prerevolutionary actor, moved to Tbilisi, Georgia where he made his first big hits: The Suram Fortress (1922, USSR) and Little Red Devils (1923, USSR) put Georgian cinema on the map.
The graph shows that those active in circles around Perestiani (who had the highest Eigenvector score of the lot) were much more connected to each other than to those in the rest of the industry, but everyone (except one person) who was connected to someone somewhere else was connected to them through Amo Bek-Nazarov. This is the second type of person that intrigues me (after the super well-connected millionaire, soldier, and hooligan Vladimir Ural’skii). Bek-Nazarov was born Ambartsum Beknazarian in Yerevan, Armenia, was trained at the Moscow Art Theater and, like Perestiani, and was a popular actor in pre-revolutionary Russian film, having worked with Evgenii Bauer in the Khanzhonkov studio.
Between 1914 and 1918, he played about 70 parts. In 1920, he went to Tbilisi where he developed a film department for the Georgian Commissioner's office of Public Education. He became head of the Georgian film studio in 1923, and he directed many films there. In 1925, he moved to Armenia and became head of Armenkino, where he made his most important films in the late 1920s. In 1933, he shot the first Armenian sound film Pepo (1935, USSR) (Widdis 2017: 172-3; Bek-Nazarov 1965: 93-117).
Karl Tomskii plays the same role for the Ukrainian-based film directors and actors, connecting the mostly isolated community to people outside of Ukraine.
That community included Petr Chardynin, the prerevolutionary star director, who returned from western Europe in 1923 to work in Odessa. In 1920, Tomskii started as assistant director, from 1921–22 he taught at the film school in Odessa, and between 1923 and 1930 he was an assistant director, director, or administrator in Odessa, Kiev, and Yalta, but he was also an administrator at Sovzapkino in Leningrad. After 1930, according to kino-teatr.ru, he dropped out of production and disappeared from view. Tomskii’s connectivity most likely came from his mobility. He acted or participated in no more than 23 films, so it is likely that his travels back and forth connected directors and actors in Ukraine with Leningrad and Moscow and made it possible for them to work together.
Tomskii and Bek-Nazarov, are bridges who create what Mark Granovetter called “weak ties.” These are connective nodes, or people in one social network who link people in another social network, which keeps a large group from becoming fragmented (Granovetter, 1973). Weak ties can also be useful in creative professions. People in densely connected communities tend to isolate themselves, like Facebook users in segregated news silos. Having a mix of weak ties (but still ties) to people outside your community makes for a more diverse set of connections to communities other than yours. Mark Granovetter has argued that weak ties between two groups of scholars, for instance, can provide connections to a greater number and diversity of people. Where strong ties in a social group probably means one is hearing the same ideas over and over again. (Granovetter, 1973; van Vugt, 2017: 29). Learning more about the institutional and creative ties provided by individuals like Tomskii and Bek-nazarov will give us a valuable new perspective on the history of the film industry.
Things change dramatically in the 1924–1929 period.
First of all, there are far more people involved in directing or acting in films (see Fig.1): up from 236 to 1956 and there are more communities, rising from 13 to 20.
The degrees of connectivity show that people have been added to the network who are less closely connected to other people in the network. The longest path between people rises from 7 to 8 steps and stays at 8 through 1934, dropping back to century-average 6 after that. But the average path between nodes/people drops from 3.5 to 3.1, a significant decrease that indicates that a significant number of people who acted in or directed films were new to Soviet film making and didn’t have the connections (or working experience) with people already working in film. At the same time, the majority of people remained closely connected to each other as a whole, despite the newcomers.
This is not surprising given that 1924–29 was a period of heightened productivity – private and state film studios opened (Mezhrabpom-Rus and Goskino/Sovkino) and there was increased funding for film making. In this period, we have a large group of experienced directors and actors as well as newcomers: now let’s look at the communities they formed and see how dispersed they are as groups.
This is where the SNA concept of communities gets interesting. In 1924–29, the number of communities increases but the modularity score goes down. Remember communities are made up of people who are more closely connected to each other than they are to other people. Modularity is a sub-measure of communities that compares people’s connections within a community with their connections with people outside their community. A higher modularity score means that among the actors and directors in the group as a whole there are more distinct communities; a higher degree of separation from people in other communities and tighter ties within the community. A lower score means more integration among communities. In other words, compared with the period 1918–23, the rapid increase in productivity in 1924 increased the number of separate groups within the industry, but they were more integrated with each other, more connected with each other than in the earlier period, even though there is still significant segregation into communities.
The other thing that is interesting in this period is the increase in the number of high Eigenvector scores, indicated on the graphs by the increase in bigger node circles, that is a higher number and distribution of people who are connected to people who are connected to a lot of people.
In this period communities of people with Georgian and Armenian surnames are still discrete, still very interconnected, and we can also see that there are few individuals there who are as connected as those in the Russian communities where we see dozens of overlapping nodes that signify people with high Eigenvector centrality scores. Another feature of the smaller, more isolated, and insular communities that we don’t see in the larger, more integrated communities is that the people who are most connected are well-known, prominent figures: Perestiani, Chiaureli, Beknazarian, and Gelovani. But look whose Eigenvector scores are marked with big circles in the Russian/Slavic groups: Gardin is still there but the other names I’ve marked here as the most connected of people in this period are not very well known. The person with the highest Eigenvector score for this period is none other than Vladimir Ural’skii. So Ural’skii, the actor with the highest Eigenvector centrality score for the period as a whole was already the most connected person in the period 1924–29. Others include actors who were in many films during this period and worked with well connected directors, including Vladimir Gardin: Sergei Minin, Alexander Gromov, Nikolai Panov, and Ivan Khudoleev.
Minin (1901–1937) was in 20 films between 1925 and 1929 (he was in a total of 41 films during his career, with release dates from 1925–38). Alexander Gromov was born in 1889 (his date of death is unknown) and was in 48 films between 1921-29. Nikolai Panov (1875–1932) was in 57 films in total, and 34 films between 1918–28 (many of which don’t survive). Ivan Khudoleev (1875–1932) was in 51 films between 1913 and 1931 (28 of which were released between 1918–1929). Khudoleev also wrote and directed one film in 1924 (kino-teatr.ru).
The pattern of corresponding rates of productivity with the number of people and connections continues in 1930–34.
A drop in productivity is accompanied by a drop in the number of nodes, a sharp drop in edges, and a drop in communities (see Figs. 1 and 18). The historical shift from a dispersed combination of private and government studios to one centralised bureaucracy overseeing production in a handful of state run studios with the introduction of Socialist Realism had the effect of decreasing productivity, a trend well known in historical studies of film production (Miller 2010; Belodubrovskaya 2017). Breaking down that observation, however, this graph of connections between directors and actors working during this period shows that the smaller number of films were made by people who were much more tightly connected to people in their dispersed communities than they were to industry as a whole. This suggests that the first stage of centralisation and lowered production had the effect of returning actors and directors to work with people they already knew in their own scattered regions.
This pattern changes radically as the effects of institutional and political centralisation in film production in the 1930s were felt. The graphs for the periods 1935–40 (Fig. 19), 1941–46 (Fig. 20), and 1947–53 (Fig. 21) clearly show different patterns of community and modularity.
As Moreno observed, the visual evidence conveyed by these graphs gives us something new to explore, something that might not be immediately clear in the raw data. The visual impact of the divergence of these data visualisations from those for the earlier periods was my first indication of a significant shift in social structures. The modularity and Eigenvector numerical scores confirm the shift, and the accuracy of the graphs, but the visualisation shows it most emphatically. As in qualitative historical scholarship, no one source is sufficient evidence to make an argument, but it is worth pointing out that the visual can alert us to significant historical developments that we might miss in other kinds of evidence.
In this case, political and institutional centralisation under Stalin in the 1930s had a noticeable impact on the social structure of the film industry. Fewer actors and directors were involved in making films and the number of connections between them also decreased, neither of which is surprising because we know that production fell significantly under Stalin.
But, at the same time, the number of communities stabilised while modularity – the relative measure of ties within a community and to the community as a whole – continued to drop, which is what we can see in these data visualisations. In the rest of this period, from 1935–53, we see much more overlap of communities and many more intersecting ties. Internal and external ties are more evenly distributed, people are moving towards more balanced connections inside their communities and to the whole; groups are becoming more integrated. We see that, in these later stages of centralisation, the communities of actors and directors become much less dispersed and much more interconnected.
In 1941 when Leningrad was under siege and Moscow was being bombed by Nazi warplanes, the two main film studios were evacuated to Central Asia, to Alma Ata (now Almaty). So it’s not surprising to see the interconnections in the war period 1941–45 (see Fig. 20). But it is significant to note that structures of social integration appeared before the war, before evacuation, and continued after it, when the studios were reconstructed and new studios were established in regions all over the country. The visual evidence shows that the interconnectedness of directors and actors began before the war, in 1935 (and continued thereafter).
The statistical data not only supports the visual evidence, it gives us some insightful detail. Figure 23 shows the increasing centralisation of communities and individuals by tracking each community’s percentage of the whole. Each line tracks the percentage of the whole represented by one of the seven largest communities (with the largest at the top).
This chart shows that during World War II the number of people in each community as a percentage of the whole becomes more evenly distributed. That is, during the war, when both the Leningrad and Moscow film studios were evacuated to Alma Ata and functioned as a combined enterprise, and actors and directors from other parts of the western Soviet Union were evacuated east to the Caucasus, Central Asia, and Siberia, created a more integrated industry. The number of people in groups that were more interconnected with each other than with the whole decreased (the top three lines) and the number of people in groups that were more integrated into the whole increased (the bottom three lines). We would expect to see more of this kind of integration during the war when so many directors and actors were evacuated together to Alma Ata, but, as noted above, the remarkable thing about these figures is that the process begins before the war and continues after the war was over (See Figs. 19–21). This longer history of connections indicates to me that the integration of previously disparate communities was a function of centralisation rather than evacuation. Furthermore, the concept of centralisation in historical and political studies takes on new qualities when we graph the structures of social and professional connections. We usually think of centralisation as the concentration of power in the geographical center and in the hands of one leader (think Moscow, Stalin) with, perhaps, a vertical replication of that concentration in the hands of regional and local leaders or administrative and institutional leaders.5 Most historians now reject totalitarian models that depict all power issuing from the top and running only in one direction, having found numerous, diverse examples of people in subordinate positions (painters, film makers, regional political leaders, and so on) capable of negotiating with their superiors for advantages. But this study of social networks shows that centralisation isn’t only about vertical power relationships and bi-lateral negotiations. And it shows that political centralisation isn’t only a set of instructions or policies, however successfully or unsuccessfully they may have been carried out. When centralisation is depicted as a set of social networks, it also creates distinct evidence of social structural integration. Centralisation creates new horizontal structures, integrating people from the periphery and forging new connections among people from different spheres. Instead of a pyramid with power at the top and everyone else dispersed at the bottom, centralisation draws dispersed communities higher up towards the apex of the pyramid where they are more concentrated, more integrated, and have new opportunities (as well as new vulnerabilities) for new contacts and connections. Centralisation of the film industry created new horizontal structures, integrating people from the periphery and forging new connections among people from different spheres.
Up until now, I’ve been describing the working connections among actors and directors in terms defined by the field of social network analysis. SNA (and thinking like a computer) is not concerned with why connections occur or how connections are affected by political and historical change, but is limited to what people do (Watts 2003: 59). I have been arguing that the actors and directors in the Soviet film industry worked together in patterns that shifted from a system that was highly segregated by ethnicity or geography to one that was both segmented and integrated. I have also been arguing that these horizontal social networks have a life of their own that seems at least partially independent of the patterns of power and influence familiar from historical studies, bringing figures to the fore who are otherwise obscure.
To understand what centralisation in these graphs means, we have to combine thinking like computers and thinking like historians or humanists. For example, we tend to think that a higher number and concentration of connections makes for a higher degree of influence and success. But the most successful and influential directors and actors don’t usually have high rates of connectedness, they tend to be closer to the mean. How can we explain this? As we saw, Seth Bernstein gives one explanation that’s important: these measures aren’t about success and influence. These are bare connections, uninflected by meaning. They’re not indications of power, they’re indications of position; in actual social terms, of putting oneself in a position to do something. Another possibility is that these horizontal connections take on a momentum of their own, somewhat independent of central power institutions and policies? Network theorists argue, for example, that once a node achieves a high degree of connectedness, that node is likely to continue to increase its degree of connectedness. In vernacular terms, one need not be a historian or an economist in the early 21st century, to see that “the rich get richer and the poor get poorer.” Whether one becomes rich through privilege or hard word, position or inheritance, or any other means, a rich person is more likely to get richer than poorer. In a well known SNA study, László Barabási and Réka Albert argued that, over time, “preferential attachment” occurs in the increase in connections among nodes in social networks. A node with twice the links of another node, will be twice as likely to receive new links as networks expand and shift (Watts 2000: 108–09, Kadushin 2012: 19–21, Barabási and Albert).
The argument about preferential attachment seems also to explain the correlation between social network centrality and longevity. The actors and directors in this period with the longest careers have high measures of centrality. Did longevity itself give an actor or director more opportunities for connection? Is this a case of preferential attachment? Or is it that the combination of high centrality measures with only average visibility or prominence provided some kind of protection in these politically fraught years?
The actor Vladimir Ural’skii provides some suggestions. Ural’skii eventually got some leading roles, but for most of his career he positioned himself to get roles in films with prominent actors and directors while remaining under the radar as an anonymous or generic character. In the late 1920s Ural’skii was on a trajectory toward more prominent roles, when one of his films was banned for political reasons. In 1928, a key transitional year with the beginning of the First Five Year Plan and Stalin’s consolidation of power, he played the lead in a high profile film, Al’bidum. This was an important film, directed by Leonid Obolenskii, with set design by Alexander Rodchenko, and written by the agronomist Petr Chayanov. Ural’skii plays a scientist-agronomist, patterned after a well-known figure of the time, head of the Academy of Agrarian Scientists, Nikolai Vavilov. But when Vavilov came under attack by Trofim Lysenko, the film was shelved, and afterwards Ural’skii’s career failed to take off, continuing as it had been before Al’bidum (Monday 2015). A number of things might have happened. Perhaps he lay low during that period to protect himself in these dangerous years. Or perhaps his high centrality scores may not mean that he was able to hide in plain sight to have a long career in a dangerous time, it may be that he was unable to get good roles after Al’bidum, as happened to many film workers involved in other controversial films. Or did he lie low because he took advantage of different kinds of connections not recorded in this study: could he have been an informer? That wouldn’t have protected him in 1937, when so many loyal party workers were caught up in the Terror. But could he have had other kinds of connections that helped him? If we want to know more about how the Soviet film industry functioned, especially in the Stalinist period, we need to return to conventional research sources – industry records, memoirs, biographical dictionaries – but we need to study the careers of the Ural’skiis and other lesser-known figures who are at the center of large groups of the famous and well-connected. The digital is so labor intensive it can be seen as an end in itself, but for me one of the key results of this study is that it raises new questions about how Soviet films were made and it points to a new cast of characters to study in conventional archival research.
Philip Bonacich, a sociologist and leading network theorist, has offered some counterintuitive suggestions useful here. Like Granovetter, Bonacich argues that weak connectivity can often be an advantage. A high Eigenvector number seems like a good ambition, because usually well-connected centrality would equate power, but not always. If you are connected to people who are not well connected, you are surrounded by people who depend on you, which equates to more, not less power. People who are well connected have options other than you (Bonacich 1987: 1171). High centrality might indicate more insular, less innovative, less prominent actors and directors, but with career longevity and a degree of self protection. In extreme circumstances, this power structure doesn’t always work, because some of the most central people in the 1920s died in 1937, closer to average ties can mean connections to people who are outside the conformist-making centers of power.
I began by pointing out that whether we study the Soviet film industry through powerful, creative individuals or through institutions, the period 1918–1953 was one of almost constant disorder and flux. The results of this study suggest that at the same time there were sources of stability and continuity. If digital ‘communities’ are similar to communities of living individuals, we can see that for the period as a whole, regional communities remained relatively stable. And even after 1935, when regional communities became more centralised and more integrated with other communities, ties among people with linguistically similar last names remained strong.
Thinking like a computer has its limitations but even this limited study does at least two useful things. It shows us the ways that people functioned as both discrete and inter-connected social networks and it raises questions worth answering by using conventional historical methods. A study of the career paths of the Ural’skiis and Bek-Nazarovs, and the changing ties among people of the Soviet film world would provide a useful approach to understanding the ways people communicated, functioned, survived, and even thrived in the film industry in these precarious years.
Joan Neuberger
University of Texas at Austin
neuberger@austin.utexas.edu
1 The literature on the early Soviet film industry in Russian, English, and European languages – institutional, documentary, auteurist, and thematic – is too extensive to fully represent in a footnote, but some selected classic and recent sources in Russian and English include Jay Leyda, Kino: A History of Russian and Soviet Film (London: Allen and Unwin, 1960); Peter Kenez, Cinema and Soviet Society, 1917–1953 (Cambridge, UK, Cambridge University Press, 1992); Birgit Beumers, A History of Russian Cinema (London: Berg, 2008); Richard Taylor, Film Propaganda: Soviet Russia and Nazi Germany (London, 1979); Naum Kleiman, Formula Final (Moscow, Eizenshtein-Tsentr, 2004); John MacKay, Dziga Vertov, Life and Work, Vol. 1, 1896–1921 (Boston: Academic Studies Press, 2018); Yuri Tsivian, Lines of Resistance: Dziga Vertov and the 1920s (Bloomington, IN: Indiana University Press, 2005); Rimgaila Salys, The Musical Comedy Films of Grigorii Aleksandrov (Bristol: Intellect, 2009); Kremlevskii Kinoteatr, 1928–1953, eds. L. Maksimenkov, et al. (Moscow: Rosspen, 2005), V. I. Fomin, Letopis’ rossiiskogo kino, 5 vols (Moscow: Maternik, 2004–2016), V. I. Fomin, Istoriia rossiiskoi kinematografii, 1941–1968, (Moscow: Kanon-Plius, 2019); Richard Taylor and Ian Christie, eds., The Film Factory: Russian and Soviet Cinema in Documents, 1896–1939 (London: Routledge and Kegan Paul, 1988); Evgenii Margolit, Zhivye i mertvoe: zametki k istorii sovetskogo kino 1920–1960-kh godov (St Petersburg: Seans 2012); Denise Youngblood, Soviet Cinema in the Silent Era (Ann Arbor, Mich.: UMI Research Press, 1985); and Movies for the Masses: Popular Cinema and Soviet Society in the 1920s (Cambridge, UK: Cambridge University Press: 1992); Jamie Miller, Soviet Cinema: Politics and Persuasion Under Stalin (London: I.B. Tauris, 2010); Maria Belodubrovskaya, Not According to Plan (Ithaca, NY: Cornell University Press, 2018; Lilya Kaganovsky, The Voice of Technology: Soviet Cinema’s Transition to Sound, 1928–1935 (Bloomington, IN: Indiana University Press, 2018); Philip Cavendish, The Men with the Movie Camera: The Poetics of Visual Style in Soviet Avant-Garde Cinema of the Silent Era (London: Berghahn, 2013); Emma Widdis, Socialist Senses: Film, Feeling and the Soviet Subject, 1917–1940 (Bloomington, IN: Indiana University Press 2018); G. Mar’iamov, Kremlevskii tsenzor: Stalin smotrit kino (Moscow: Kinotsentr, 1992).
2 Grant Packard, et al. 2016. “The Role of Network Embeddedness in Film Success,” International Journal of Research in Marketing 33 (2016); Park, S., Oh, K. & Jo, G. “Social network analysis in a movie using character-net.” Multimed Tools Appl 59, 601–627 (2012). https://doi.org/10.1007/s11042-011-0725-1; Chung-Yi Weng, Wei-Ta Chu, and Ja-Ling Wu, “RoleNet: Movie Analysis from the Perspective of Social Networks,” IEEE Transactions on Multimedia 11:2 (February 2009). For applications of SNA in History, see the online journal Historical Network Research http://historicalnetworkresearch.org/bibliography/#Art%20History
3 The databases were prepared by Ryan Williams; I could not have done this study without his help. All graphs and charts in this article were made by the author.
4 Kino-teatr.ru has some limitations. Some films made during the early period were lost, actors and directors are occasionally incorrectly identified, and webscraping tools for collecting the data from the website and translating it into spreadsheets are not perfectly accurate. The scale of the data to some extent mitigates these inaccuracies, but while the graphs and charts that I present here have the appearance of mathematical accuracy, they should be seen as indicators of general trends.
5 Centralisation and the tension between centralisation and the dispersal of power is the main political question of the early Soviet and Stalinist periods. For a short introduction: David L. Hoffmann, The Stalinist Era (Cambridge, UK 2018); for a classic view of centralisation: Oleg V. Khlevniuk, Master of the House: Stalin and his Inner Circle (New Haven, CT, 2008) and Stalin: New Biography of a Dictator (New Haven, CT, 2015); on the replication of power structures at the regional level, J. Arch Getty, Practicing Stalinism: Bolsheviks, Boyars, and the Persistence of Tradition (New Haven, CT, 2013). Almost any social history of the period could offer alternative networks, studies of domestic space for example.Recently anthropologists and others have studied such social networks in a variety of productive ways that should be applied historically: see for example, Unraveling Ties: From Social Cohesion to New Practices of Connectedness, Eds Yehuda Elkana, Ivan Krastev, Elisio Macamo, and Shalini Randeria (Chicago, 2002); Maria Sidorkina, “Shining a Light” on Us and Them: Public-Making in Ordinary Russia,” Ab Imperio 2015 (2): 209-251; Alena V. Ledeneva. How Russia Really Works: The Informal Practices That Shaped Post-Soviet Politics and Business (Ithaca, 2006).
Joan Neuberger is Professor of History at The University of Texas at Austin. Her most recent book is This Thing of Darkness: Eisenstein’s Ivan the Terrible in Stalin’s Russia (Ithaca: Cornell University Press, 2019). She is Editor of the online public history website, Not Even Past and co-host of the podcast 15 Minute History. Her current project is a part-digital, part-textual study, entitled, Global Eisenstein: Immersion in Nature, Art, and the World.
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