Social network analysis also distinguishes between ‘‘cohesion’’ and ‘‘equivalence’’as the basis for sub-groups. The cohesion approach suggests that sub-groups are based
on the density of direct dyadic ties. Hence, the greater the number of ties within a
group, the more cohesive it should be. By contrast, the equivalence approach argues
that sub-groups will be composed of actors with equivalent ties to third parties.
Marx’s analysis of class formation is a classic example: workers are brought together
not by their direct solidaristic ties, but by their common opposition to employers.
The distinction between cohesion and equivalence is related to a broader set of
discussions in network analysis. Research on what came to be known as the ‘‘small
world phenomenon’’ discovered that people were often connected to quite distant
others through a surprisingly short number of intervening steps. As Watts ( 2003 ) has
clariWed, this is most surprising when networks are relatively ‘‘sparse.’’ Watts found
that small world networks have particular properties. They exhibit high local
clustering combined with a limited number of ‘‘shortcuts’’ between clusters.
Granovetter ( 1973 ) also built on the small world phenomenon in his inXuential
argument about the ‘‘strength of weak ties.’’ He found, for instance, that jobs were
often not found directly through friends (strong ties), but through friends of friends
(weak ties). The logic is that weak ties often ‘‘bridge’’ across clusters. Burt ( 1992 ) has
further reWned this logic in his work on ‘‘structural holes.’’ He argues that informa-
tion in small tightly knit clusters is redundant (everybody knows everybody’s
business). Moreover, clustering creates ‘‘holes’’ in the global network that limit the
Xow of information. Thus, ties that bridge across structural holes (‘‘shortcuts’’ in
Watt’s terms, ‘‘weak ties’’ in Granovetter’s) are powerful conduits of information.
The cohesion perspective suggests that the critical mechanism in networks oper-
ates through direct dyadic ties. An extension of this logic suggests that the stronger
the tie (e.g. the more frequent, intimate, and intense the interaction), the more
cohesive the relationship. At the global network level, then, a denser network is
presumed to be a more cohesive one. The logic extends to multiple networks.
Network analysis refers to the situation in which two actors are tied together in
diVerent types of ways—for example friendship, advice, co-work, residence—as
multiplexity. In the cohesion logic, the more multiplex the network, the stronger it
is. By contrast, the equivalence perspective emphasizes the importance ofindirectas
well as direct ties. Actors are similar not because they have strong ties to one
another, but because they have similar ties to others. Actors who are structurally
equivalent are therefore interpreted as having a similar position in the network.
Multiple networks are important when they reinforce structural equivalence.
The diYculty of collecting network data has been one of the limits on the more
widespread usefulness of social network methods. Two basic classes of network
data exist.Egocentric networksbegin with a focal actor or actors (ego) and then
collect network information on relationships of ego to others (alters). A later phase
of data collection collects further information on the relationships between ego’s
alters. The general problem with egocentric data is that it is highly selective, since
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