Marks 2018, Social Network Research in Africa

African Affairs, 1–17

doi: 10.1093/afraf/ady067

© The Author(s) 2018. Published by Oxford University Press on behalf of Royal African Society. All rights reserved

RESEARCH NOTE SOCIAL NETWORK RESEARCH IN AFRICA ZOE MARKS* AND PATRYCJA STYS

ABSTRACT Social network approaches (SNA) have much to offer for the study of African politics. This research note explores the tensions and benefits of using social networks as metaphor or as method, and highlights the types of questions network research can address. We discuss sources of network data, key features of the graphical perspective and basic vocabulary, and the difference between analyzing individual networks and full systems.

Three SNA concepts—centrality, brokerage, and multilevel networks— indicate theoretic spaces for qualitative and quantitative synergies. The note also raises practical considerations and ethical challenges for conducting network research in fieldwork settings, drawing on a collaborative project in eastern Democratic Republic of the Congo. In conclusion, we encourage layering disciplines and mixing methods to more fully understand how networks shape social life in Africa.

SOCIAL NETWORK RESEARCH SEEMS TO BE in a state of perpetual renaissance. From the late 1950s when anthropologists began vigorously documenting urban social structures alongside kinship diagrams,1 to contemporary analyses of how people sell mobile phone top-up south of the Sahara,2 its ability to link structure and agency feels perennially *Zoe Marks (zoe_marks@hks.harvard.edu) is a Lecturer of Public Policy at the Harvard Kennedy School, Harvard University. The author gratefully acknowledges valuable feedback on earlier drafts of the manuscript and related research from the editors and two anonymous reviewers; generous colleagues Cassy Dorff, Anders Themnér, and Erica Chenoweth; project collaborators Paul Nugent and Christine Bell; and Congo experts whose roles and responsibilities are detailed on the project Technical Brief (available from the lead author), but whom for security precautions are not named herein. This research was funded by the ESRCDFID Poverty and Conflict project (Poverty Alleviation Research Grant es/m009130/1) and the Political Settlements Research Programme (www.politicalsettlements.org; a consortium of five organizations supported by UK Aid), Department for International Development, for purposes of alleviating of poverty; nothing herein represents the views of the funders.

Patrycja Stys is a Research Officer at the Center for Public Authority and International Development at the London School of Economics.

1. Elizabeth Colson, ‘Social control and vengeance in Plateau Tonga society’, Africa 23, 3 (1953), pp. 199–212.

2. Laura Mann and Elie Nzayisenga, ‘Sellers on the street: The human infrastructure of the mobile phone network in Kigali, Rwanda’, Critical African Studies 7, 1 (2015), pp. 26–46.

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fresh. Social networks underpin power across African societies, yet often serve as a poisoned chalice, encouraging trust and reciprocity on the one hand, while on the other, exacerbating horizontal inequality, marginalization, and corruption. However, social network approaches are often contested and confused due to the ambiguous meaning of a ‘social network’ and inconsistency in whether the term is being used as a metaphor, heuristic, or formal analytic model. The fact that social network research is well suited to a diverse range of interdisciplinary debates and subfields can compound the confusion. Adopting common language and new tools with which to analyze networks will sharpen interdisciplinary research across African politics.

Drawing on the latest developments and new directions in social network research, this note discusses what network approaches have to offer —as a conceptual framework and methodological approach—for the study of African politics. We explore tensions and benefits of using social networks as metaphor or as method, and highlight the types of questions network research can address. The second section looks at the nuts-and-bolts of data collection and analysis, and introduces key concepts from social network analysis (SNA) to help bridge the gap between formal graphical and heuristic approaches. The third section raises practical considerations and ethical challenges for conducting network research in fieldwork settings, drawing on a collaborative project in eastern Democratic Republic of the Congo. In conclusion, we encourage layering disciplines and mixing methods to more fully understand how networks shape social life in Africa.

Networks as theory and method in African politics Today’s network research echoes long-standing debates about human interconnectedness, from ancient mythology to twitter.3 A social network is simply a set of defined relationships among actors in a social system. Yet, many social scientists use the phrase ‘social network’ to refer vaguely to people who know each other, or other somewhat more abstractly to people and other entities (organizations, institutions, businesses), without defining network boundaries or features. Over the years, the divide has widened between formal analytic approaches, which quantitatively measure relational ties; and interpretive or qualitative approaches that use ‘social network’ as a heuristic to explore how meanings, movements, and processes are facilitated by and flow through relationships.4 Using the term ‘social 3. Tanja Bosch, ‘Twitter activism and youth in South Africa: The case of #RhodesMustFall’, Information, Communication & Society 20, 2 (2017), pp. 221–232.

4. Michael D. Ward, Katherine Stovel, and Audrey Sacks, ‘Network analysis and political science’, Annual Review of Political Science 14 (2011), pp. 245–264.

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network’ as a metaphor can be appropriate and illuminating in many cases, but the more technical methodology of ‘SNA’ focuses specifically on structures of relationships among clearly defined social units.5 For Africanist research, these approaches are compatible and complementary. As J. Clyde Mitchell notes in the first volume of fieldwork-based social network research in Africa, network analysis is ‘complementary to and not a substitute for conventional … frameworks of analysis’.6 Across the analyticmetaphoric spectrum, all network approaches focus on how social structures—hierarchies, asymmetries, clusters, cleavages, personal networks and more—pattern social behaviour. Aspects of network theory can sharpen our analysis of social dynamics across a range of debates in African politics, from articulating and measuring patronage networks, to tracing information diffusion; mapping conflict and cooperation, or resource exchanges and flows.

Analyzing the types of ties linking actors is particularly valuable for understanding informal norms. A paradigmatic network metaphor, JeanFrancois Bayart’s conception of the ‘rhizome state’ describes African states as webs of invisible power structures connecting actors and interests below the surface of formal institutions.7 Similar theories about the ‘shadow state’ and ‘big man’ politics abound, drawing attention to the ‘vertical network of patron–client relations’.8 These networks are highly dynamic and instable, as patrons and clients alike seek to broker better deals across social and political space, fomenting competition and causing alliances to break apart.9 As Robert Jackson and Carl Rosberg wrote in 1984, ‘in terms of methodology the image of personal rule draws our attention not only to rulers and their activities, but also to the political networks, circumstances, and predicaments in which they are entangled’.10 In Ghana’s parliament, Anja Osei uses SNA to reveal networks underpinning institutionalized politics that transcend party and ethnicity and facilitate interand intra-party bargaining: ‘The crisscrossing of party affiliation and ethnic

5. Stanley Wasserman and Katherine Faust, Social network analysis: Methods and applications (Cambridge University Press, Cambridge, 1994), pp. 3–4.

6. James Clyde Mitchell, ‘The concept and use of social networks’, in James Clyde Mitchell (ed.), Social networks in urban situations: Analyses of personal relationships in Central African towns (Manchester University Press, Manchester, 1969), pp. 1–50, p. 8.

7. Jean-François Bayart, The state in Africa: The politics of the belly (Longman, London, 1993).

8. William Reno, Corruption and state politics in Sierra Leone (Cambridge University Press, Cambridge, 1995); Robert H. Jackson and Carl G. Rosberg, ‘Personal rule: Theory and practice in Africa’, Comparative Politics 16, 4 (1984), pp. 421–442, p. 422.

9. Thanks go to Anders Themner for encouraging us to emphasize this crucial point. See Alex De Waal, ‘Mission without end? Peacekeeping in the African political marketplace’, International Affairs 85, 1 (2009), pp. 99–113; Mats Utas (ed.), African conflicts and informal power: Big men and networks (Zed Books, London, 2012).

10. Jackson and Rosberg, ‘Personal rule’, p. 422.

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identity at the elite level serves as a major stabilizing factor for Ghana’s democracy…Group solidarities … strengthen elite unity and reduce the likelihood of ethnic mobilization.’11 Documenting a densely connected, consensually united elite network subverts the common assumption that personal ties necessarily destabilize formal institutions, and echoes Peter Ekeh’s theory of two publics, wherein political actors fulfil different norms across social spaces.12 In democratic and non-democratic regimes alike, network approaches can identify tactics of personal rule. A multilevel analysis of political appointments under Ethiopia’s Haile Selassie from 1941 to 1974 reveals how the emperor shuffled political appointees to prevent rival networks forming. Josef Woldense demonstrates how 72 per cent of reassignments were to wholly different branches, breaking immediate networks while cultivating communities of expertise.13 In Togo SNA reveals an opposite phenomenon: stable elite networks have sustained the rule of Faure Gnassingbé, son of long-standing dictator Eyadema, despite gradual reforms and the younger Gnassingbé’s perceived charismatic deficits.14 Comparing Ethiopia and Togo side by side reveals alternative models of authoritarian stability, with Selassie relying on shuffling and Gnassingbé benefitting from a long-serving ethnic cabal. Conversely, across regime types network research demonstrates the stabilizing effect of elite networks in democratic Ghana and semi-authoritarian Togo. Further research can sharpen our understanding of how identities and relationships shape governance, distribution of power, and the function and durability of regime types.

Network analysis is particularly useful to see beneath institutional surfaces in contexts where political order is contested or in transition. Social network concepts have been invoked heuristically to describe the political economy of conflict in the Sahel and Somalia, where business networks and social trust determine resource flows in ‘ungoverned’ spaces.15 Judith Scheele’s historical ethnography of the Sahara uses network-as-metaphor 11. Anja Osei, ‘Elites and democracy in Ghana: A social network approach’, African Affairs 114, 457 (2015), pp. 529–554, pp. 553–554.

12. Anja Osei and Thomas Malang. ‘Party, ethnicity, or region? Determinants of informal political exchange in the parliament of Ghana’, Party Politics 24, 4 (2016), pp. 410–420; Peter Ekeh, ‘Colonialism and the two publics in Africa: A theoretical statement’, Comparative Studies in Society and History 17, 1 (1975), pp. 91–112, p. 93.

13. Josef Woldense, ‘The ruler’s game of musical chairs: Shuffling during the reign of Ethiopia’s last emperor’, Social Networks 52 (2018), pp. 154–166.

14. Anja Osei, ‘Like father, like son? Power and influence across two Gnassingbe presidencies in Togo’, Democratization 25, 8 (2018), pp. 1460–1480.

15. Morten Bøås, ‘“Castles in the sand”: Informal networks and power brokers in the Northern Mali periphery’, in Mats Utas (ed.), African conflicts and informal power: Big men and networks (Zed Books, London, 2012), pp. 119–134; Aisha Ahmad, ‘The security bazaar: Business interests and Islamist power in civil war Somalia’, International Security 39, 3 (2015), pp. 89–117, p. 95.

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to explain how trade is embedded in moral economies and socio-political genealogies that transcend state borders.16 Combining ethnography and network analytic concepts of brokerage and reciprocity, Anders Themnér examines how former commanders become gatekeepers of political stability and job opportunities in postwar Liberia.17 Yet, just as vertical and horizontal ties can provide order, they are also highly fluid and can play a key role in mobilizing violence. This makes civil conflict networks rife with interdependent relationships—such as alliances or attacks—between violent and non-violent actors that change over time, reshaping the conflict system.18 Coups and terrorism are routinely understood in terms of co-conspirator networks, and quotidian networks can be just as deadly.19 Social network surveys in Rwanda suggest that personal ties increased pressure for people to participate in genocide; and recent cross-national evidence suggests mobile technologies may amplify bottom-up collective action among Africa’s diffuse networks.20 Network research at the most local level can illumine how and why political cleavages and economic inequalities persist. Often understood in the context of social capital, personal networks are powerful predictors of opportunity and upward mobility.21 Multiple studies in African cities and rural areas have shown that education, training, and skills do not necessarily lead to increased economic status.22 SNA can demonstrate that having many connections is not the same as being well connected.23 16. Judith Scheele, Smugglers and saints of the Sahara: Regional connectivity in the twentieth century (Cambridge University Press, Cambridge, 2012).

17. Anders Themnér, ‘Former military networks and the micro-politics of violence and statebuilding in Liberia’, Comparative Politics 47, 3 (2015), pp. 334–353; Anders Themnér and Mats Utas, ‘Governance through brokerage: Informal governance in post-civil war societies’, Civil Wars 18, 3 (2016), pp. 255–280.

18. Cassy Dorff, Max Gallop, and Shahryar Minhas, ‘Networks of violence: Predicting conflict in Nigeria’, Journal of Politics (forthcoming), pre-print available at: https://static1. squarespace.com/static/570d5281e32140b109e3bb50/t/5bec1b1888251b5df9b8d25b/ 1542200092040/confEvo.pdf (18 December 2018).

19. Philip Roessler, Ethnic politics and state power in Africa: The logic of the coup-civil war trap (Cambridge University Press, Cambridge, 2016).

20. Omar McDoom, ‘Antisocial capital: A profile of Rwandan genocide perpetrators’ social networks’, Journal of Conflict Resolution 58, 5 (2014), pp. 865–893; Jan H. Pierskalla and Florian M. Hollenbach, ‘Technology and collective action: The effect of cell phone coverage on political violence in Africa’, American Political Science Review 107, 2 (2013), pp. 207–224, c.f. p. 209.

21. Marcel Fafchamps and Bart Minten, ‘Returns to social network capital among traders’, Oxford Economic Papers 54, 2 (2002), pp. 173–206.

22. Wassie Kebede and Alice K. Butterfield, ‘Social networks among poor women in Ethiopia’, International Social Work 52, 3 (2009), pp. 357–373; Getahun Fenta Kebede and Francesca Odella, ‘The economic returns of network resources to the urban informal economy: Evidence from street vendors in Addis Ababa, Ethiopia’, European Journal of Sustainable Development 3, 3 (2014), pp. 357–372.

23. Jean-Philippe Berrou and François Combarnous, ‘The personal networks of entrepreneurs in an informal African urban economy: Does the “strength of ties” matter?’ Review of Social Economy 70, 1 (2012), pp. 1–30.

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Research in borderland markets, for example, shows that traders connected to state representatives and politicians do better than those who rely on less valuable links to religious elites.24 Across the continent, gender and age stack the deck socially in ways that disadvantage women’s and young people’s ability to access high status individuals.25 As Kate Meagher shows in southeast Nigeria, these structural inequalities vary as individual attributes—such as gender, age, ethnicity, education, and class —interact with social norms and network resources.26 Research among single female-headed households in Addis Ababa’s informal settlements indicates that poorer, younger women are more likely to have large diverse social networks out of necessity—relying often on casual or kin ties, as they seek patron ties—while older more skilled women have better odds of embedding themselves in elite circles, reflecting and compounding inequality, even within marginalized communities.27 Much of this research raises an important challenge to Granovetter’s seminal theory on the ‘strength’ of weak ties, wherein acquaintances connect people to opportunities28: in Africa’s informal markets, entrepreneurship requires resilience and the economic safety net provided by strong ties from kinship and co-ethnic networks.29 Thus, with important implications for power and politics, network research in Africa is revealing what types of social assets and structures lead to government stability, local peace, and improved individual livelihoods. The following section presents analytic tools and concepts key to SNA research that can also be adapted for qualitative heuristic approaches.

Possibilities for network data collection and analysis One of the great strengths of the social network perspective is its conceptual precision. However, network terminology can also be off-putting to outsiders, obscuring otherwise straightforward concepts. Moreover, network analysis is often critiqued for being data-driven and skewing toward 24. Mathias Kuépié, Michel Tenikue, and Olivier J. Walther, ‘Social networks and small business performance in West African border regions’, Oxford Development Studies 44, 2 (2016), pp. 202–219.

25. This aligns with social network and privilege patterns globally, see: Nan Lin, ‘Social networks and status attainment’, Annual Review of Sociology 25 (1999), pp. 467–487.

26. Kate Meagher, Identity economics: Social networks & the informal economy in Nigeria (Boydell & Brewer, Woodbridge, 2010).

27. Kebede and Butterfield, ‘Social networks among poor women in Ethiopia’; Matthew Desmond, ‘Disposable ties and the urban poor’, American Journal of Sociology 117, 5 (2012), pp. 1295–1335.

28. Mark Granovetter, ‘The strength of weak ties’, American Journal of Sociology 78, 6 (1973), pp. 1360–1380.

29. Berrou and Combarnous, ‘The personal networks of entrepreneurs’.

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description rather than explanation.30 Connecting qualitative, theoretically rich research with systematic empirical approaches can address this shortcoming and mutually advance the fields of social network research and African studies. This section discusses some of the tools and concepts that can facilitate applications of SNA in Africa, as well as how Africabased research can expand social network analysis.31 I highlight sources of network data, key features of the graphical perspective and basic vocabulary, and the difference between analyzing individual networks and full systems. Three SNA concepts—centrality, brokerage, and multilevel networks—indicate theoretic spaces for qualitative and quantitative synergies.

Formal SNA studies are only just emerging in many African contexts because of the difficulty of collecting relational data.32 However, much like actor-focused data, social network data can be collected through a range of sources and methods, including existing documents—such as news reports or historical archives—surveys, interviews, and participant observation or ethnography. Selecting the appropriate data collection technique is a matter of data availability and the research question at hand. In studying the social structures underlying regime stability, for instance, Osei worked with Ghanaian and Togolese researchers to interview present-day parliamentarians,33 while Woldense collected historical network data from archives in Addis Ababa.34 Several studies have used network surveys, from traders in Sahelian borderlands and Madagascan markets, to farmers in Eastern Congo and Rwanda.35 Even in liminal spaces, such as conflictaffected states, researchers have successfully used ethnography to broker trust within networks, or triangulated organizational ties through inferences drawn with other datasets, such as ACLED and UCDP.36 New text 30. Mustafa Emirbayer and Jeff Goodwin, ‘Network analysis, culture, and the problem of agency’, American Journal of Sociology 99, 6 (1994) pp. 1411–1454.

31. Excellent introductions to SNA include: Stephen P. Borgatti, Martin G. Everett, and Jeffrey C. Johnson, Analyzing social networks (SAGE Publications Limited, New York, NY, 2013); Garry Robins, Doing social network research: Network-based research design for social scientists (SAGE Publications Limited, New York, NY, 2015); Wasserman and Faust, Social network analysis.

32. For a bibliography, see also, Olivier Walther, ‘Social Network Analysis in sub-Saharan Africa’, Olivier J. Walther Blog (n.d.). Available at: https://olivierwalther.net/sna/ (18 December 2018).

33. Osei, ‘Like father, like son?’; Osei ‘Elites and democracy in Ghana’.

34. Woldense, ‘The ruler’s game of musical chairs’.

35. Olivier J. Walther, ‘Business, brokers, and borders: The structure of West African trade networks’, Journal of Development Studies 51, 5 (2015), pp. 603–620; Fafchamps and Minten, ‘Returns to social network capital among traders’; Martha Ross, Leveraging social networks for agricultural development in Africa (Wageningen University, unpublished PhD dissertation, 2017); McDoom, ‘Antisocial capital’.

36. Themnér, ‘Former military networks and the micro-politics of violence and statebuilding in Liberia’; Dorff et al., ‘Networks of violence’; Olivier Walther, Christian Leuprecht, and David Skillicorn, ‘Networks and spatial patterns of extremist organizations in North and West Africa’, in Olivier Walther and William Miles (eds), African border disorders (Routledge, London, 2017), pp. 60–86.

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scraping techniques are expanding the possibilities for studying massive quantities of news reports and digital data. For example, researchers using a semi-automated coding process analyzed 7 years of reporting in the Sudan Tribune to capture highly granular geo-spatial patterns of violence, unveiling interaction patterns between ethnic groups and the environment.37 As they note, data sourced from English language news reporting has obvious limitations, but the breadth and depth of coverage would also be ‘nearly impossible to obtain in the field’.38 Similarly, data scraping from multiple news outlets can facilitate network analysis of highly visible but notoriously elusive elites, such as public co-appearances between Nigerian presidents Goodluck Jonathan and Muhammudu Buhari, and appointees to the national petroleum board.39 Despite diverse methods and sources, all of these studies use relational ties—not individual actors or nodes—as the primary unit of analysis. While methodological individualism focused on individuals’ motives, decisions, and actions predominates in the social sciences, the interdependence of individuals and groups that gives rise to social behaviour is widely recognized.40 Social networks enable or constrain opportunities and actions. The smallest network, a dyad, consists of just two actors; that tie, a relationship between two nodes, can have a number of distinguishing features. Belying its foundations in graph theory, in SNA individual actors are referred to as nodes, points, vertices, or actors. Relational ties, in turn, are called links, edges, arcs, or ties. As with actor attributes, relational ties have their own features in network data that are often reduced (imperfectly) to variables for the purposes of comparative analysis. For example, ties can be coded as: positive or negative, reflecting affinity or hostility; directed, moving to or from a node; weighted, e.g. by distance, frequency, or duration of the link; and characterized categorically, e.g. by nature of the relationship, such as family, neighbour, or coworker. Asking, ‘who do you go to for advice?’ solicits a positive directed tie (A goes to B; but B goes to C, not A, for advice); perhaps A and B are family—a strong tie, while B and C are colleagues—a weak tie. Migration studies have used network data to understand how relationships affect who migrates, why, and how resources circulate. A social network survey shows remittances (a directed tie) do not just flow from the 37. Tracy Van Holt, Jeffrey C. Johnson, James D. Brinkley, Kathleen M. Carley, and Janna Caspersen, ‘Structure of ethnic violence in Sudan: A semi-automated network analysis of online news (2003–2010)’, Computational and Mathematical Organization Theory 18, 3 (2012), pp. 340–355.

38. Ibid, p. 352.

39. Paasha Mahdavi, ‘Scraping public co-occurrences for statistical network analysis of political elites’, Political Science Research and Methods (2017), pp. 1–8.

40. John W. Patty and Elizabeth Maggie Penn, ‘Network theory and Political Science’, in Jennifer Nicoll Victor, Alexander Montgomery, and Mark Lubell (eds), The Oxford handbook of political networks (Oxford University Press, Oxford, 2017), pp. 147–171, p. 147.

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Ghanaian diaspora home, but are cyclical and reciprocal, with families also sending money to émigrés abroad.41 Studies with Senegalese and Congolese migrants show that women more often rely upon strong kinship ties, while men draw on weak ties (friends and acquaintances), leading to ‘significant differences in migration experiences, settlement patterns, and the relationships they maintain with origin communities.’42 Information about actor attributes and relational ties can map either personal networks or a full-system network. Personal networks, called egocentric networks or ego-nets in network parlance, are particularly prominent in studies related to social capital and individual or household outcomes. The African–European migration study above uses household survey data about migrants’ personal networks to understand how network composition—especially strong and weak ties—shapes experiences of transnational migration. Economic research has also relied on ego-net data to understand how social connections affect entrepreneurs’ success across Africa.43 A full-system approach, in contrast, seeks to map every tie linking all actors in a given social network. This addresses a key tenet of network theory: actors and their relationships are interdependent in a social system, and missing nodes or edges can dramatically alter the structure of a network.44 Therefore, arguably the two most important decisions in designing full network research are determining (i) the type(s) of relational ties of interest and (ii) the network boundary.45 Of the two methods for determining a network boundary, the most straightforward is to use a predefined census capturing all nodes in a discrete social system—such as residents of a village, members of parliament, or traders in a geographic marketplace—and then mapping relational ties within the entire population.46 The alternative approach is to use a cascade or snowball, wherein first-wave ‘seed’ respondents are identified according to pre-determined criteria; then, all of the nodes (called alters) in their ego-net are 41. Valentina Mazzucato, ‘Informal insurance arrangements in Ghanaian migrants’ transnational networks: The role of reverse remittances and geographic proximity’, World Development 37, 6 (2009), pp. 1105–1115.

42. Liu Mao-Mei, ‘Migrant networks and international migration: Testing weak ties’, Demography 50, 4 (2013), pp. 1243–1277; Sorana Toma and Sophie Vause, ‘Gender differences in the role of migrant networks: Comparing Congolese and Senegalese migration flows’, International Migration Review 48, 4 (2014), pp. 972–997, p. 973.

43. E.g. Berrou and Combarnous, ‘The personal networks of entrepreneurs’; Fafchamps and Minten, ‘Returns to social network capital among traders’; Kuépié et al., ‘Social networks and small business performance’.

44. Skyler J. Cranmer and Bruce A. Desmarais, ‘Inferential network analysis with exponential random graph models’, Political Analysis 19, 1 (2011), pp. 66–86, p. 67.

45. Edward O. Laumann, Peter V. Marsden, and David Prensky, ‘The boundary specification problem in network analysis’, Research methods in social network analysis (Transaction Publishers, London, 1989), pp. 61–87.

46. Perhaps the first census design in Africa is Bruce Kapferer, Strategy and transaction in an African factory (Manchester University Press, Manchester, 1972).

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interviewed, and so on, until data has been collected from all actors fitting the inclusion criteria. This method has been used to map mobile money flows in family networks across rural and urban Kenya, and to trace disease transmission in contact networks.47 However, research questions that impinge on structural features (such as centrality and brokerage discussed below) require full network data, which can be difficult to gather when networks are hidden or covert, too large, or when the boundary and members are unknown.

Because people/nodes are not randomly located in network structures, nor randomly connected to one another, full-system network analysis is focused on measuring dependencies between actors.48 New data collection methods and statistical techniques are improving analysis of partially observed networks, wherein not all nodes and edges can be captured, and expanding the potential for statistical inference with network data.49 Exponential Random Graph Modelling (ERGM) is one widely used approach for modelling social networks in order ‘to describe parsimoniously the local selection forces that shape the global structure of a network’.50 While mathematically advanced, ERGMs and other approaches, such as latent space models and additive and multiplicative effects models, can account for social structural patterns—such as homophily (similar actors relate to each other), transitivity (ready connection between A and C if they both connect to B), clustering (network areas with dense ties), and network change over time—that decades of social science research has demonstrated are important, but which are still poorly accommodated in standard linear models.51 Latent space models have the added advantage of accounting for the structure without the analyst having to specify relational

47. Sibel Kusimba, Yang Yang, and Nitesh Chawla, ‘Hearthholds of mobile money in western Kenya’, Economic Anthropology 3, 2 (2016), pp. 266–279, p. 266.

48. Cassy Dorff and Michael D. Ward, ‘Networks, dyads, and the social relations model’, Political Science Research and Methods 1, 2 (2013), pp. 159–178, p. 163.

49. Johan H. Koskinen, Garry L. Robins, Peng Wang, and Philippa E. Pattison, ‘Bayesian analysis for partially observed network data, missing ties, attributes and actors’, Social Networks 35, 4 (2013), pp. 514–527; Mark S. Handcock and Krista J. Gile, ‘Modeling social networks from sampled data’, The Annals of Applied Statistics 4, 1 (2010), pp. 5–25, p. 8.

50. David R. Hunter, Mark S. Handcock, Carter T. Butts, Steven M. Goodreau, and Martina Morris. ‘ergm: A package to fit, simulate and diagnose exponential-family models for networks’, Journal of Statistical Software 24, 3 (2008), nihpa54860.

51. Miller McPherson, Lynn Smith-Lovin, and James M. Cook, ‘Birds of a feather: Homophily in social networks’, Annual Review of Sociology 27 (2001), pp. 415–444; Daniel K. Sewell and Yuguo Chen, ‘Latent space models for dynamic networks’, Journal of the American Statistical Association 110, 512 (2015), pp. 1646–1657; Nils W. Metternich, Cassy Dorff, Max Gallop, Simon Weschle, and Michael D. Ward, ‘Antigovernment networks in civil conflicts: How network structures affect conflictual behavior’, American Journal of Political Science 57, 4 (2013), pp. 892–911, pp. 902–903; Shahryar Minhas, Peter D. Hoff, and Michael D. Ward, ‘Inferential approaches for network analyses: AMEN for latent factor models’, arXiv:1611.00460 (v2. 2018).

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dependencies.52 Such models can measure the probability of a tie occurring in a network—such as the likelihood of war between two actors—or, the likelihood of the network structure itself changing as actor attributes shift.53 Network methods have been adopted most readily in the study of conflict and security, where systems and dyadic relationships are already well theorized.

Other network concepts are prominent in African studies and political science more broadly, but have not been analysed with social network approaches. Centrality and brokerage are arguably the most intuitive network concepts ripe for adoption in African politics research. Centrality, an indicator of social connectedness, is an important tool for studying social power and influence and can be measured in four ways. The most straightforward measure, degree centrality, refers to the number of ties connecting a node to other nodes; high degree centrality indicates a ‘popular’ actor. In African politics, a classic ‘big man’ has high in-degree centrality: many nodes come to him or her for favours or advice; a chief or queen mother listens to many grievances or petitions. As a result, patronage also gives her high out-degree centrality (how many ties emanate from a node toward others). She can ‘speak’ to many people, particularly other elites to whom her clients are less likely to have access, while people living in poverty have high out-degree centrality due to maintaining connections to a broad range of individuals just to get by. SNA can also reveal alternative positions of influence in a social network, where number of ties alone does not necessarily make an actor the most socially important. Closeness centrality measures proximity to other points by counting the number of edges a node has to travel to reach all other nodes in the network.

Betweenness centrality on the other hand indicates how important a node is for linking other actors by measuring how many paths pass through it to connect other nodes to each other.54 Finally, eigenvector centrality takes into account how influential (by degree) are a given node’s connections.55 These measures have been used to examine questions related to information and resource flows, and to understand nuances of power and influence in a network. Several agricultural development studies in Africa have tested different centrality measures to discover the most effective strategies for disseminating inputs and technologies to improve crop

52. The authors are indebted to Cassy Dorff, a brilliant methodologist, for explaining this distinction.

53. Walther and Miles, African border disorders.

54. Stephen P. Borgatti, ‘Centrality and network flow’, Social Networks 27 (2005), pp.

55–71.

55. Phillip Bonacich, ‘Some unique properties of eigenvector centrality’, Social Networks 29, 4 (2007), pp. 555–564.

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yields. Their findings also offer insights—and new questions—for understanding local and informal politics in rural Africa.56 In Ethiopia, businesses with high degree centrality (more direct connections to other businesses) have better profits, regardless of skills or closeness to other businesses in the network57; similarly, farmers with larger networks, correlated with being from a dominant ethnic group, benefit more from extension schemes and social learning.58 In DRC, Ross and colleagues find high centrality actors are less effective than network brokers at reaching households on network margins.59 As a node that bridges communities in the network, gatekeepers/brokers can expand a network by bringing in nodes that are otherwise outside a given subgroup and increase network density. For example, in Olivier Walther’s borderland research, one market featured high levels of embeddedness wherein Nigerians were doing business almost exclusively with other Nigerians, rather than with Beninois or Nigerien traders. As a result, the network relied on bridging ties from a few powerful brokers.60 A neighbouring market with no structural holes did not rely on gatekeepers, because traders had been historically well-connected across identity groups.61 More importantly, brokerage positions enable actors to monopolize or manipulate information and resource flows in a network, sometimes deciding what passes through a network—and at what cost. As a result of their structural positions, brokerage is a key empirical and analytical tool for understanding mobilization, violence and alliance, and statebuilding.62 Brokerage can make social networks sensitive to exogenous events. For example, the death or departure of a key gatekeeper can alienate entire subsets in a network. Similarly, the loss of an actor with high degree centrality can make a network sparser, requiring longer paths to connect nodes. While this may make networks seem fragile and fleeting, many SNA studies find that social life reorganizes according to common patterns, such as reciprocity and network triads facilitating friendship and collaboration.

56. Lori Beaman, Ariel BenYishay, Jeremy Magruder, and Ahmed Mushfik Mobarak, ‘Can network theory based targeting increase technology adoption’, Unpublished manuscript (2015), Available at: http://www.ferdi.fr/sites, www.ferdi.fr/files/pictures/beaman_et_al_-can_ network_theory-based_targeting_increase_technology_adoption.pdf (28 July 2018).

57. Ishiwata Ayako, Petr Matous, and Yasuyuki Todo, ‘Effects of business networks on firm growth in a cluster of microenterprises: Evidence from rural Ethiopia’, RIETI Discussion Paper 14 (2014).

58. Petr Matouš, Yasuyuki Todo, and Dagne Mojo, ‘Roles of extension and ethnoreligious networks in acceptance of resource-conserving agriculture among Ethiopian farmers’, International Journal of Agricultural Sustainability 11, 4 (2013), pp. 301–316.

59. Ross, Leveraging social networks for agricultural development in Africa.

60. Walther, ‘Business, brokers and borders’.

61. Ibid.

62. Ronald S. Burt, Brokerage and closure (Oxford University Press, Oxford, 2005).

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Finally, an important concept for bridging qualitative and quantitative research, and for sharpening our understanding of African social systems, is the multilevel network. Multilevel networks use two or more levels of aggregation to examine interactions between individual actors, and the organization/collective units in which they are embedded. Multilevel approaches can clarify the nature of moral obligations, trust, and reciprocity arising from kinship, co-ethnicity, or other in-group political dynamics.63 Strong within group pressure, or ‘bonding capital’, can lead to trust and support within, but exclusion and violence outside or at the margins of a group; whereas bridging capital can link groups and facilitate cooperation. Such two-mode analysis has illustrated resource management strategies among communities that share or compete for water resources and grazing land.64 For example, ethnographic analysis of all community members and raiding parties among pastoralist Nyangatom shows that a small number of leaders initiate cattle raids, but friendship ties among age-sets determine who participates.65 Multilevel analysis could measure the relative importance of various types of network membership—such as ethnolinguistic group, trade/ profession, neighbourhood, or class—in shaping social behaviour. And, while advanced methods are available to adapt ERGMs and stochastic actor-oriented models for multilevel networks, qualitative and interpretive research can also benefit from, and contribute theoretically to, examining effects of individual actors’ embeddedness in organizational structures. As network approaches evolve, they inspire new ways to examine existing data and new questions in their own right. The following section discusses some practical challenges and ethical considerations for designing mixed methods projects and collecting network data in the field.

Practical considerations for social network fieldwork Our current research brings a social network perspective to post-conflict peacebuilding and ex-combatant reintegration.66 Existing social network research on armed conflict has focused on how networks facilitate 63. Mark Granovetter, ‘Economic action and social structure: The problem of embeddedness’, American Journal of Sociology 91, 3 (1985), pp. 481–510.

64. Malick Faye, ‘Social networks and institutions in self-governance systems: Water supply management in northwestern Senegal’ Unpublished paper (2012), Available at: https:// dlc.dlib.indiana.edu/dlc/bitstream/handle/10535/8630/Social%20Networks%20and%20Institutions %20in%20Self-Governance%20Systems.pdf?sequence=1 (29 July 2018).

65. Luke Glowacki, Alexander Isakov, Richard W. Wrangham, Rose McDermott, James H. Fowler, and Nicholas A. Christakis, ‘Formation of raiding parties for intergroup violence is mediated by social network structure’, Proceedings of the National Academy of Sciences 11, 43 (2016), pp. 12114–12119.

66. ESRC-DfID Poverty Alleviation Research Grant es/m009130/1 (led by Zoe Marks and Paul Nugent, in collaboration with Jan Eichhorn and Patrycja Stys) with additional funds provided by the DfID Political Settlements Research Programme (led by Christine Bell).

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violence.67 We explore the other side of this social process: how armed group participation shapes livelihoods and social life when war subsides.

Using a stratified link-tracing design, our research measures social integration patterns and how wartime networks affect access to economic and social support after war. To include a range of actors in a shared social system, we selected a relatively safe and secure field site in eastern DRC with ongoing mobilization, but not active fighting, and large numbers of demobilized combatants. We focused on three groups of interest: former rebels from the Second Congo War; militia members; and civilians who did not identify as members of any armed group, and stratified our sample by gender and mobilization status. These groups have different organizational structures, mobilization trajectories, and political profiles.

Community gatekeepers necessarily facilitated our access for research, and the network perspective incorporates their social influence in the research design. Alternative approaches, such as asking the chief for a roster to sample ex-combatants—a strategy used elsewhere—posed unobserved selection bias and ethical risk amidst continued insecurity. We adapted link-tracing (like chain-referral or snowball sampling), which is widely used in qualitative and mixed methods research,68 and one of the best methodological tools for partially observing networks.69 These and other adaptive strategies, like respondent-driven sampling, draw on ‘information collected during the survey to direct subsequent sampling’ to generate theoretically and empirically rigorous insights.70 Seed informants are interviewed in wave one and their named contacts/alters are interviewed in subsequent waves until the entire network has been mapped or until a pre-determined sampling threshold has been met.71 We stratified initial informants and used actor selection and relationship inclusion criteria to determine waves of interviewees and the types of relationships to be captured or excluded.72 Such hybrid designs can turn what would be empirically and analytically toxic bias in statistical inference into an analytical strength for understanding social structure. Endogenous dependencies

67. McDoom, ‘Antisocial capital’; Anders Themnér, ‘A leap of faith: When and how excombatants resort to violence’, Security Studies 22, 2 (2013), pp. 295–329.

68. Patrick Biernacki and Dan Waldorf, ‘Snowball sampling: Problems and techniques of chain referral sampling’, Sociological Methods & Research 10, 2 (1981), pp. 141–163.

69. Peter V. Marsden, ‘Network data and measurement’, Annual Review of Sociology 16, 1 (1990), pp. 435–463, pp. 439–440.

70. James Coleman, ‘Relational analysis: The study of social organizations with survey methods’, Human Organization 17, 4 (1958), pp. 28–36.

71. For an introduction to walks, trails, and paths, see Robert A. Hanneman and Mark Riddle, Introduction to social network methods (University of California Riverside, Riverside, CA, 2005); Handcock and Gile, ‘Modeling social networks from sampled data’, pp. 9–11.

72. Laumann et al., ‘The boundary specification problem in network analysis’, p. 63.

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are not a problem to be avoided, but rather a feature to be analyzed. As a first attempt to collect comparative ex-combatant social network data in Africa (or elsewhere, to our knowledge), the project created a partially observed network dataset that can be used to examine individual patterns of integration and social support, as well as to model theoretically the full network and explore missing links.73 We used a life history survey that included three name generator exchange questions, asked with participatory network diagrams. Interviewees shared names of people with whom they spend time socially and go to for advice or emotional support, and whom they would approach for a loan or job.74 In addition to these exchange questions, we collected basic attribute data for their named alters and information about their relationship: the length of time people knew each other, how they met, the frequency of their interactions, and whether the person is considered a friend, professional contact, or both. Original network data collection is a time consuming, resource intensive process that presents logistical challenges for boundary-specification and identifying all nodes and edges. It is often impractical to interview every alter because link-tracing waves can grow exponentially; nonresponse and refusal to participate can significantly impact the observed network; and road and cell phone infrastructure can be far-flung and unreliable. In our project, the mobility of conflict-affected communities compounded boundary-specification challenges. People are frequently displaced in eastern DRC. Population flows and social structural changes over time raise important questions for new research; so, too, do missing ties and nodes.

For example, how does the death of a commander affect social network structure; does it matter if she dies during or after war? We limited our focus in this project to mapping the current personal networks of living individuals, rather than full-system or time series data.

Social network data collection is not widely used, which presents some additional challenges. Enumerators need to be trained in the concepts and methodological rationale even and perhaps especially if they have experience implementing randomized population surveys. How data are collected and the type of information generated is different from individual or household surveys, especially if respondents’ alters (friends and contacts) will be interviewed in subsequent waves. Trust and rapport were particularly important in our project, and were facilitated by a talented,

73. Zoe Marks, ‘Gender, armed groups, and social networks’, feminists@law 8, 2 (2019); Zoe Marks, ‘From violent capital to social capital? Ex-combatant networks in Eastern Congo’, (unpublished manuscript) presented at Sustainable Peace research working group, Folke Bernadotte Academy, Monrovia, Liberia, November 2018.

74. Full details are laid out in the project ‘Technical brief: DR Congo’, available from the author.

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adaptive team in the field. Qualitative interviews can complement network survey research by revealing hidden dynamics affecting collection and interpretation of social data.75 While we were able to address methodologically many of the challenges presented by the DRC context and research topic, ethical challenges and tradeoffs remain that affect any social network study. It is very difficult to anonymize quantitative and qualitative network data, which can be both highly sensitive and highly specific.76 Identifying just one node—for example, a broker or actor with high degree centrality—can connect to other parts of the network. This has implications for data collection, storage, analysis, publishing, and dissemination. Network researchers often collect data about individuals who are not interviewed directly, complicating free and informed consent; and, scholars may need to retain individually identifiable information into the data cleaning phase to accurately map relational ties. At minimum, nodes should be anonymized and data encrypted as soon as is practical. It might be necessary to anonymize field sites and not publish full-system network data. Depending on the sensitivity of the social network and data being collected (e.g. covert, illicit, or activist networks), researchers may want to implement preemptive protective measures. For example, network holes could be deliberately constructed by not collecting data in all sites; or, data could be collected in more sites than the published sample, so the site is unidentifiable even if researchers were visible in target communities. Limiting or duplicating data collection brings its own ethical tradeoffs, as it may weaken the analytical and applied value of the findings. Social network analysis is likely to be most valuable to network members, who can use information about actors and relationships in their midst to pursue their own objectives.77 This has implications for sharing and returning data to the communities where research is conducted, a cornerstone of ethical fieldwork practice.

Repressive regimes and security actors can exploit network data for crackdowns (much ‘dark network’ research is supported by counterterrorism funding, for example), and it may be impossible to protect network identities in contexts with high-capacity intelligence communities. Researchers need to foreground these ethical tradeoffs at the research design stage,

75. Insa Nolte, Rebecca Jones, Khadijeh Taiyari, and Giovanni Occhiali, ‘Exploring survey data for historical and anthropological research: Muslim–Christian relations in south west Nigeria’, African Affairs 115, 460 (2016), pp. 541–561.

76. Bin Zhou, Jian Pei, Wo-Shun Luk, ‘A brief survey on anonymization techniques for privacy preserving publishing of social network data’ (Technical Report TR 2008–16, Simon Fraser University, Vancouver, BC, 2008).

77. David Krackhardt, ‘Assessing the political landscape: Structure, cognition, and power in organizations’, Administrative Science Quarterly (1990), pp. 342–369.

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thinking carefully about to whom network data is valuable and how it can be used.78

Conclusion Social network research and its formal analytical perspective is an invaluable tool for social scientific research in Africa. Its emphasis on social systems as comprised of interrelated actors captures the essence of social life, facilitating a clear-eyed understanding of both structure and flow.

Because its concepts can be used with a range of data collection and research methods, a network perspective is particularly well suited to interdisciplinary communities like African studies. African studies can contribute theoretic and methodological innovation to network studies on informal and hidden networks, the relative benefits of strong and weak ties, patterns of privilege and exclusion, and diffusion of information and resources across social spaces.

Formal SNA also presents an opportunity to strengthen our empirical base on issues that have been prominent but at times ambiguously substantiated in African politics, such as patron–client dynamics and ethnic politics. It can clarify how socially constructed categories of gender, ethnicity, religion, and more have material and structural impacts on lived experience. It lends a richer conceptual vocabulary to how individual behaviour is embedded in organizational and community networks, and can shed light on how geographic and technological infrastructure shape social action patterns. It provides a robust way to understand what is not random and cannot be controlled in experimental designs. Perhaps most ambitiously, relational data has the potential to help us explore the interaction effects of rational and emotional motives. Network perspectives can be used to address social and economic inequalities, improve public health interventions, strengthen peace and justice norms, and sustainably manage environmental resources. Indeed, in Africa, social network research is already being applied to each of these issues.

78. Nissim Cohen and Tamar Arieli, ‘Field research in conflict environments: Methodological challenges and snowball sampling’, Journal of Peace Research 48, 4 (2011), pp. 423–435; Charles Kadushin, ‘Who benefits from network analysis: Ethics of social network research’, Social Networks 27, 2 (2005) pp. 139–153; Jenine K. Harris, ‘Consent and confidentiality: Exploring ethical issues in public health social network research’, Connections 28, 2 (2008), pp. 81–96.