the pyramid. However, many discussions, particularly in organization theory,
suggest that networks are diVerent from hierarchies. As pointed out by Kontopou-
los ( 1993 ), the diVerence is that hierarchies are distinguished by ‘‘many-to-one’’
relationships, in which many subordinates are linked to only one superordinate.
A network by contrast is an ‘‘entangled’’ web of relationships characterized by
‘‘many-to-many’’ relationships. Ansell ( 2000 ) uses this many-to-many criterion to
characterize regional (subnational) policy in Europe.
Thus, a network can be distinguished both by the content of relationships
(positive recurrent relations, built on mutual obligation, aVection, trust,
and reciprocity, etc.) and by its global structure (interconnected dyads, many-
to-many relationships).
3 Network Analysis
.........................................................................................................................................................................................
One of the distinguishing features of network institutionalism is the availability of
a range of quantitative techniques designed to analyze the properties of networks.
The development of these techniques grew out of the use of graph theory to
represent networks, though much recent network analysis also draws on algebraic
methods. It is beyond the scope of this article to provide more than a cursory
discussion of these methods. However, several book-length introductions are
available. Scott ( 1998 ) and Degenne and Forse ́ ( 1999 ) provide useful surveys
of social network analysis and Wasserman and Faust ( 1994 ) provide a compre-
hensive, but more mathematically demanding treatment. Several software pro-
grams are also available for social network analysis, of which the most popular is
UCINET.
Prominent techniques of social network analysis include centrality and
‘‘sub-group’’ identiWcation. Centrality is a particularly useful measure because it
identiWes the relative importance or prominence of individual actors in a network
based on information about all the actors in the network. Various measures of
centrality have been developed (degree, closeness, betweenness, etc.) that seek to
capture diVerent aspects of what it means to be a central actor. For example,
betweenness centrality deWnes centrality in such a way as to identify actors
likely to serve as important brokers. Another class of network techniques identify
‘‘sub-groups’’ within the network and they are particularly useful for identifying
social cleavages or factions. These techniques range from those that identify
sub-groups in relatively inclusive terms (e.g. component analysis) to those that
are much more restrictive (e.g. clique detection).
78 christopher ansell