303
16 A Network Perspective on Governing Interactions
mathematics to public health (Barabasi 2002 ; Cross and Parker 2004 ) . In social
systems they consist of nodes, which may be individuals, groups of individuals or
organizations, and the ties between them. The ties may be unidirectional or bidirectional. They can be described in many ways, depending on what aspect of the interaction is of interest. For example, they may consist of information, advice, cooperation
in activities and exercise of authority. The use of networks in the analysis of policymaking and governance is very much an emerging fi eld in which there is a need to
develop both theory and a body of case studies that will allow concepts and theories
to be tested (Raab and Kenis 2007 ) .
Social network analysis can be used to examine the extent and nature of the ties
among network nodes. Social network analysis seeks to describe, understand and
explain the social relationships among nodes by examining the interactions within
the network ( Brandes and Wagner 2004 ). It can be used to assess communication,
cooperation and other inter-organizational environments of the network (Jorgensen
2004 ). Until recently, however, it has not been prominent in discussions of adaptive
governance and resilience (Carlsson and Sandström 2008; Bodin and Crona 2009 ) .
Although governability from a network perspective involves understanding the
nature of both the nodes and the ties, emphasis is placed on the latter as these represent the interactions in the interactive governance approach. Conventional analyses
of node attributes alone, which do not include the relationships among nodes, run
the risk of missing much that can inform our emerging understanding of the nature
of governability. We can draw conceptual or mental maps of the relationships that
we think exist, or ought to exist, among entities engaged in governance. This use of
‘network’ as more of a metaphor is often the initial stage in an analysis. However,
the complexity of interactions and structural arrangements, even in a network with
a just few handfuls of nodes, is such that quantitative analysis is required to reveal
patterns and uncover the hidden properties of the network that qualitative analysis
cannot fully describe.
Various software packages are available for social network analysis, with
UCINET being one of the most widely used (Borgatti et al. 2002 ) . Network diagrams
or sociograms used to visualize (graphically represent) the network are created with
NetDraw, a component of UCINET 6.0. Many important network metrics, particularly of large and complex structures, require computational power to be derived
and analyzed statistically. Computer applications greatly assist visualization by
using a suite of algorithms to provide options for how nodes and ties among nodes
can be made to appear in sociograms. For very small networks, the analysis and
graphics can be done manually. For large ones, the use of software is essential to
convert the data tables into diagrams that are meaningful. The size, shape and color
of the symbols used to depict nodes can represent various attributes. Relationship
type, strength and direction can be shown by line color, thickness and the use of
arrowheads. For very large networks, graphics may not be useful and analysis must
be based on metrics alone.
Social network analysis uses a variety of network metrics to describe network
structure. For purposes of illustration, we focus on two network measures that relate
to the nature of and limitations to interaction in the network: density and centrality.
16 A Network Perspective on Governing Interactions
mathematics to public health (Barabasi 2002 ; Cross and Parker 2004 ) . In social
systems they consist of nodes, which may be individuals, groups of individuals or
organizations, and the ties between them. The ties may be unidirectional or bidirectional. They can be described in many ways, depending on what aspect of the interaction is of interest. For example, they may consist of information, advice, cooperation
in activities and exercise of authority. The use of networks in the analysis of policymaking and governance is very much an emerging fi eld in which there is a need to
develop both theory and a body of case studies that will allow concepts and theories
to be tested (Raab and Kenis 2007 ) .
Social network analysis can be used to examine the extent and nature of the ties
among network nodes. Social network analysis seeks to describe, understand and
explain the social relationships among nodes by examining the interactions within
the network ( Brandes and Wagner 2004 ). It can be used to assess communication,
cooperation and other inter-organizational environments of the network (Jorgensen
2004 ). Until recently, however, it has not been prominent in discussions of adaptive
governance and resilience (Carlsson and Sandström 2008; Bodin and Crona 2009 ) .
Although governability from a network perspective involves understanding the
nature of both the nodes and the ties, emphasis is placed on the latter as these represent the interactions in the interactive governance approach. Conventional analyses
of node attributes alone, which do not include the relationships among nodes, run
the risk of missing much that can inform our emerging understanding of the nature
of governability. We can draw conceptual or mental maps of the relationships that
we think exist, or ought to exist, among entities engaged in governance. This use of
‘network’ as more of a metaphor is often the initial stage in an analysis. However,
the complexity of interactions and structural arrangements, even in a network with
a just few handfuls of nodes, is such that quantitative analysis is required to reveal
patterns and uncover the hidden properties of the network that qualitative analysis
cannot fully describe.
Various software packages are available for social network analysis, with
UCINET being one of the most widely used (Borgatti et al. 2002 ) . Network diagrams
or sociograms used to visualize (graphically represent) the network are created with
NetDraw, a component of UCINET 6.0. Many important network metrics, particularly of large and complex structures, require computational power to be derived
and analyzed statistically. Computer applications greatly assist visualization by
using a suite of algorithms to provide options for how nodes and ties among nodes
can be made to appear in sociograms. For very small networks, the analysis and
graphics can be done manually. For large ones, the use of software is essential to
convert the data tables into diagrams that are meaningful. The size, shape and color
of the symbols used to depict nodes can represent various attributes. Relationship
type, strength and direction can be shown by line color, thickness and the use of
arrowheads. For very large networks, graphics may not be useful and analysis must
be based on metrics alone.
Social network analysis uses a variety of network metrics to describe network
structure. For purposes of illustration, we focus on two network measures that relate
to the nature of and limitations to interaction in the network: density and centrality.