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’” (1957:12).
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
relationships, prescribing expected rights and
duties of the actors occupying diverse positions
- Sexual & 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.
LARGE KINSHIP NETS
Anthropologist Douglas White’s PGRAPH program
analyzes large kinship networks. Couples and their
uncoupled children are vertices, while parent-child arcs
connect nodes within & between different nuclear
families. Pgraph handles five key issues: bounded
subgraphs, cohesion, size, social relations, 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.”
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 transferring
college students meet and form friendships This
interpretation relies on network summary measures,
or aggregated block-models, to show social change
operating 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.”
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 the hanger-on nodes 10 and 15) never seems to settle on a
particular nomination pattern, but changes nominations steadily over the observation period.”
All my friends are so small town …
Community network analysts explore the small worlds inside
huge urban agglomerations that keep anomie at bay. Two major
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).
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 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.”
Interviewer collects data about each alter’s
social attributes (gender, race, age,
occupation,…), then asks: “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
JUST THE FACTS, MA’M
The 1985 GSS module uncovered many factoids about the
average personal networks of American adults:
• 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 welleducated, and those living in larger places” (Marsden 1987:130).
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 the strong ties of blood
relations & social group identities. Outsiders
gain entry only if 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 is a constant threat.
CLIENTALISTIC CULTURES
Clientalistic systems are prevalent in Mediterranean, Asian, and
Latin American cultures with heavily collectivist conceptions of
social organization, such as Confucian or Catholic 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 & lower strata.
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-deMoll. 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.