KIN, FRIENDS, and COMMUNITY

1. KIN, FRIENDS, and COMMUNITY Anthropologists were among the earliest developers of social network ideas to study kinship patterns of pre-industrial societies and small rural communities. Moreno, of course, invented sociograms to map children’s friendship patterns. And many sociological network analysts studied interpersonal ties within large modern communities. The ethnographic research tradition remains a robust contributor to social network analysis today. Siegfried F. Nadel argued that the role system of a society forms the matrix of its social structure: “We arrive at the structure of a society through abstracting from the concrete population and its behaviour the pattern or network (or ‘system’) of relationships obtaining ‘between actors in their capacity of playing roles relative to one another’.” (Nadel 1957:12)

2. Kinship Networks Network approaches to kinship examine the structure of marriage rules and the strategies for social bonding across generations. • Kinship involves complex interlocked role relations, prescribing expected rights & duties of the actors occupying distinct positions • Sexual relations & reproduction rights • Child-rearing obligations • Dowries, land & property inheritance • Cohesion/solidarity & collective action In Anatomy of Kinship (1963) Harrison White used matrix algebra to simplify complex classificatory rules regarding marriage and parentage. These rules generate clans among people in the same kinship situation. The resulting classificatory kinship system operates as an abstract group. White showed how to classify existing clan systems into a few basic types, revealing a wider variety of clan systems than anthropologists had previously hypothesized.

3. Large Kinship Nets Anthropologist Douglas White’s PGRAPH program analyzes large kinship networks. Couples and their uncoupled children are vertices, while parent-child lines connect nodes within and between different nuclear families. Pgraph handles bounded size, subgraphs, cohesion, social relations, and groups. White et al. (1999) applied Pajek to represent Pgraph genealogies: “Relinking of families through marriage, for example, can be formally defined as sets of bounded groups that are the cohesive cores of kinship networks, with nodes at various distances from such cores. The structure of such cores yields an analytic decomposition of kinship networks and constituent group and role relationships.”

4. Friendship on Frat Row Theodore Newcomb (1953) started a fraternity at Bennington College in the 1930s. In return for free room and board, 17 fratmen filled out weekly sociometric rankings. These 15 NEWFRAT matrices, stored in UCINET, are a classic dataset on the evolution of friendship choices. The usual story about Newcomb’s fraternity is that structural convergence occurred as those transferring college students met and formed friendships. That interpretation comes from network summary measures, or aggregated blockmodels, indicating that structural change operates through structurally equivalent actors.  However, convergence remains a controversial conclusion, because as much as one-fifth of the friendship ties changed during in the final weeks. Moody et al. (2004) use network “movies” to argue that “the overall structure does not converge on a single form, and that the process of change is heterogeneous with some actors forming stable relations while others dance between friends throughout the observation period.”

5. An Evolving Network “Two groups follow a simple convergence story -- with their nominations getting progressively more stable as time passes. The first of these groups … has 7 members, including the cluster at the right of the movie (1,6,13,8) and presents a gradual convergence of nomination patterns, while the second (with 6 members) does not converge on stable nomination patterns until week 5. Finally, group 3 (with 4 members, including hanger-on nodes 10 & 15) never seems to settle on a particular nomination pattern, but changes nominations steadily over the observation period.”

6. ♪♫ All My Friends Are So Small Town … Community network analysts explore the small worlds inside huge urban agglomerations that keep anomie at bay. Two early exemplars were Claude Fischer’s study of personal networks in Northern CA & Barry Wellman’s project in East York, Toronto. “Small-town respondents tended to be more involved with relatives, city respondents with nonkin. … urban residence apparently discouraged involvement with kin, especially extended kin. … Urbanism seemed to similarly discourage involvement and encourage selectivity with neighbors.” (Fischer 1982:258) Wellman found half of intimates were relatives, with kin and nonkin spread over a wide area. Most ties were to people living in the city, but very few were based in East York. “Communal” links were neither solidaristic nor localized. People had “sparsely knit” (low density) networks lacking in cross-linkages, so support and help in everyday matters and in emergencies was limited. “East Yorkers can almost always count on help from at least one of their intimates, but they cannot count on such help from most of them” (Wellman 1979:1217).

7. Name Generators Instruments that measure ego-centric networks in community or national surveys must use an open-ended “name generator” rather than an enumerated checklist. The 1985, 1987 & 2004 GSS quex: “From time to time, most people discuss important matters with other people. Looking back over the last six months, who are the people with whom you discussed matters important to you. Just tell me their names or initials.” For each pair of alters: “Are (Name) and (Name) total strangers? Especially close?” After asking about every alter’s gender, race, age, occupation, etc.: “Here is a list of some of the ways in which people are connected to each other. Some people can be connected to you in more than one way. For example, a man could be your brother and he may belong to your church and be your lawyer. When I read you a name, please tell me all the ways that person is connected to you.” →→►► Spouse, parent, sibling, child, other family, coworker, member of group to which you belong, neighbor, friend, professional advisor or consultant, other

8. Just the Facts, Ma’m The 1985 GSS module uncovered many factoids about the social composition of adult Americans’ egocentric discussion networks • Median size = 3 alters; 25% have 0-1 alters, 25% have 5-6 • Half of alters are ego’s kin; only 20% have no kin in their networks • Alters know one another: mean density = 0.61; only 5% all strangers • High race/ethnic homogeneity; only 8% have any alter diversity • Substantial sex diversity: 78% have at least one alter of opposite sex (most often a spouse, sibling, or parent) • “The GSS survey network data describe relatively small, kin-centered, dense, homogeneous social environments surrounding Americans. … To the extent that success of ‘networking’ as an instrumentally oriented pursuit depends on access to diverse others, those best situated to make use of it are the young and middle-aged, the well-educated, and those living in larger places” • (Marsden 1987:130). • The 2004 GSS survey found significantly smaller ego-nets & more isolates.

9. Networking, Chinese Style Guanxi: “personal relations or connections. … One’s guanxi network is seen as an appropriate response to the uncertainties posed by China’s cumbersome bureaucracy” (Yi & Ellis 2000) Guanxi is based on strong ties of blood relations & social group identities. Outsiders gain entry only when a mutual friend vouches. Key drivers: saving “face” and accumulating favors owed (renqing) – “a never-in-balance personal ledger of debits and credits rather than prompt repayment of outstanding debts.” By relying on unequal personal obligations, guanxi networks reduce transaction costs, mistrust, and deceitful opportunism. Thus, efficient economic exchanges can occur outside formal organizations and social institutions, helping China to make its transition to a market economy. Guanxi benefits: business opportunities; information on changes in governmental policies; resources such as land or import licenses. But, without a strong rule-of-law, corruption remains a constant threat.

10. Clientalistic Cultures Clientalistic systems are prevalent in Mediterranean, Asian, and Latin American cultures having heavily collectivist conceptions of social organization, such as Catholic or Confucian ethics. “Patron-client systems combine strong emotional, particularistic ties with simultaneous but unequal exchanges of different types of resources. … Clients exchange personal loyalty, deference, and awe for the protection, understanding, and material benefits provided by their patrons.” (Knoke 1990:142). Most cliques and entourages surrounding a patron are modeled after patriarchal clans and extended families. Kinship forms the inner hub, grounded in familial intimacy and trust. The spokes are friends and acquaintances who perform brokerage services, manipulating others’ resources for their own profit. The result is a hierarchical status structure connecting higher and lower strata.

11. References Fischer, Claude. 1982. To Dwell Among Friends: Personal Networks in Town and City. Berkeley, CA: University of California Press. Knoke, David. 1990. Political Networks. New York: Cambridge University Press. Marsden, Peter V. 1987. “Core Discussion Networks of Americans.” American Sociological Review 52:122-131. Moody, James, Daniel McFarland, Skye Bender-de Moll. 2004. “Dynamic Network Visualization: Methods for Meaning with Longitudinal Network Movies.” (Downloaded October 2, 2004) <www.sociology.ohio-state.edu/jwm/NetMovies/Sub_CD/dynamic_nets_public.html> Nadel, S. F. 1957. The Theory of Social Structure. London: Cohen & West. Wellman, Barry. 1979. “The Community Question: The Intimate Networks of East Yorkers.” American Journal of Sociology 84:1201-1231. White, Douglas R., Vladimir Batagelj and Andrej Mrvar 1999. “Anthropology: Analyzing Large Kinship and Marriage Networks with Pgraph and Pajek.” Social Science Computer Review 17(3):245-274. White, Harrison C. 1963. An Anatomy of Kinship: Mathematical Models for Structures of Cumulated Roles. Englewood Cliffs, NJ: Prentice-Hall. Yi, Lee Mei and Paul Ellis. 2000. “Insider-Outsider Perspectives of Guanxi.” Business Horizons 43:25-30.