5th Annual Central Florida Community Partners Nonprofit Conference

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The Use of Network Analysis to Strengthen
Community Partnerships

• Naim Kapucu, Ph.D.Department of Public
  AdministrationCollege of Health and Public
  AffairsUniversity of Central FloridaE-mail
  nkapucu_at_mail.ucf.edu

5th Annual Central Florida Community Partners
Nonprofit Conference Collaboration The Power of
Partnerships May 11, 2006
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Objectives

• Social Network Analysis (SNA)
• Using SNA in Community Capacity Building
• SNA Applications (discussions)
• SNA Software Demonstration (UCINET)
• Examples of SNA
• Student performance
• Nonprofit governance
• Partnerships (in emergencies)
• Central Florida nonprofits
• Conference participants

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The Social Network Approach

• The world is composed of networks - not
  densely-knit, tightly-bounded groups
• Networks provide flexible means of social
  organization and of thinking about social
  organization
• Networks have emergent properties of structure
  and composition
• Networks are a major source of social capital
  mobilizable in themselves and from their contents
• Networks are self-shaping and reflexive

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Social Relations

• Social relations can be thought of as dyadic
  attributes. Whereas mainstream social science is
  concerned with monadic attributes (e.g., income,
  age, sex, etc.), network analysis is concerned
  with attributes of pairs of individuals, of which
  binary relations are the main kind. Some examples
  of dyadic attributes
• Kinship brother of, father of
• Social Roles boss of, teacher of, friend of
• Affective likes, respects, hates
• Cognitive knows, views as similar
• Actions talks to, has lunch with, attacks
• Distance number of miles between
• Co-occurrence is in the same club as, has the
  same color hair as
• Mathematical is two links removed from

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Social Network Analysis( SNA)

• Social network analysis is the study of social
  entities (called actors), and their interactions
  and relationships
• The interactions and relationships can be
  represented with a network or graph
• each vertex (or node) represents an actor
• each link represents a relationship
• From the network, we can study the properties of
  its structure, and the role, and position of each
  social actor
• We can also find various kinds of sub-graphs,
  e.g., communities formed by groups of actors
• Set of Connected Units People, Organizations,
  Networks
• Can Belong to Multiple Networks
• Examples Friendship, Organizational,
  Inter-Organizational, World-System, Internet

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Social Network Analysis (SNA)

• SNA is the mapping and measuring of relationships
  and flows between people, groups, organizations,
  animals, computers, or other information/knowledge
  processing entities
• The nodes in the network are the people and
  groups while the links show relationships or
  flows between the nodes
• SNA provides both a visual and a mathematical
  analysis of human relationships
• A method to understand networks and their
  participants is to evaluate the location of
  actors in the network. Measuring the network
  location is finding the centrality of a node.
  These measures help determine the importance, or
  prominence, of a node in the network. Network
  location can be different than location in the
  hierarchy, or organizational chart
• We will look at a social network (Kite Network)
  developed by David Krackhardt. Two nodes are
  connected if they regularly talk to each other,
  or interact in some way

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What is Network Analysis?

• Network analysis is the study of social relations
  among a set of actors
• In the process of working in this field, network
  researchers have developed a set of distinctive
  theoretical perspectives as well. Some of the
  hallmarks of these perspectives are
• focus on relationships between actors rather than
  attributes of actors
• sense of interdependence a molecular rather
  atomistic view
• structure affects substantive outcomes
• emergent effects
• Network theory is sympathetic with systems theory
  and complexity theory
• Social networks is also characterized by a
  distinctive methodology encompassing techniques
  for collecting data, statistical analysis, visual
  representation, etc.

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Data Sources of SNA

• Questionnaires
• Direct observation
• Written records
• Experiments
• Affiliations and similarities
• Online resources

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Basics of Network Measures

• Centrality
• Degree
• Closeness
• Betweenness
• Flow Betweenness
• Cliques Sub-groups
• N-cliques
• N-Clans

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Kite Network
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Centrality

• Structural attributes of nodes in a network
  (position)
• Measure of the contribution of network position
  to the importance, influence, prominence of an
  actor in a network
• Centralization refers to the extent to which a
  network revolves around a single node

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Degree Centrality

• Number of direct ties to others (Row or column
  sums of adjacency matrix)
• Important or prominent actors are those that are
  linked or involved with other actors extensively
• A person with extensive contacts (links) or
  communications with many other people in the
  organization is considered more important than a
  person with relatively fewer contacts
• The links can also be called ties. A central
  actor is one involved in many ties
• Common wisdom in personal networks is the more
  connections, the better. This is not always so.
  What really matters is where those connections
  lead to -- and how they connect the otherwise
  unconnected

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Betweenness Centrality

• If two non-adjacent actors j and k want to
  interact and actor i is on the path between j and
  k, then i may have some control over the
  interactions between j and k
• Betweenness measures this control of i over
  other pairs of actors. Thus,
• if i is on the paths of many such interactions,
  then i is an important actor

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Betweenness Centrality

• Loosely, the number of geodesic paths that pass
  through a node. The number of times that any
  node need a given node to reach any node by the
  shortest path
• While Diane has many direct ties, Heather has few
  direct connections -- fewer than the average in
  the network. Yet, in may ways, she has one of the
  best locations in the network -- she is between
  two important constituencies. She plays a
  broker role in the network. The good news is
  that she plays a powerful role in the network,
  the bad news is that she is a single point of
  failure. Without her, Ike and Jane would be cut
  off from information and knowledge in Diane's
  cluster
• A node with high betweenness has great influence
  over what flows in the network. As in Real
  Estate, the golden rule of networks is
  Location, Location, Location
• Flow Betweenness Centrality

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Closeness Centrality

• The graph-theoretical distance of a given node to
  all other nodes (The sum of the rows/columns of
  the geodesic distance matrix of a graph)
• Simple closeness is an inverse measure of
  centrality the larger the numbers, the more
  distance an actor is, and the less central
  (farness!)
• Fernando and Garth have fewer connections than
  Diane, yet the pattern of their direct and
  indirect ties allow them to access all the nodes
  in the network more quickly than anyone else.
  They have the shortest paths to all others --
  they are close to everyone else. They are in an
  excellent position to monitor the information
  flow in the network -- they have the best
  visibility into what is happening in the network

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Network Centralization

• Individual network centralities provide insight
  into the individuals location in the network.
  The relationship between the centralities of all
  nodes can reveal much about the overall network
  structure
• A very centralized network is dominated by one or
  a few very central nodes. If these nodes are
  removed or damaged, the network quickly fragments
  into unconnected sub-networks. A highly central
  node can become a single point of failure. A
  network centralized around a well connected hub
  can fail abruptly if that hub is disabled or
  removed. Hubs are nodes with high degree and
  betweeness centrality
• A less centralized network has no single points
  of failure. It is resilient in the face of many
  intentional attacks or random failures -- many
  nodes or links can fail while allowing

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Cliques and Sub-groups

• Networks are also built up out of the combining
  of dyads and triads into larger, but still
  closely connected sub-structures
• Many of the approaches to understanding the
  structure of a network emphasize how dense
  connections are compounded and extended to
  develop larger cliques or sub-groupings
• A clique is simply a sub-set of actors who are
  more closely tied to each other than they are to
  actors who are not part of the group
• This view of social networks focuses attention on
  how connection of large networks structures can
  be built up out of small and tight components

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Network Reach

• Not all network paths are created equal. More and
  more research shows that the shorter paths in the
  network are more important (key paths in networks
  are 1 and 2 steps and on rare occasions, three
  steps)
• The small world we live is not one of "six
  degrees of separation" but of direct and indirect
  connections lt 3 steps away. Therefore, it is
  important to know who is in your network
  neighborhood? Who are you aware of, and who can
  you reach?
• In the Kite Network, who is the only person that
  can reach everyone else in two steps?

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Boundary Spanners

• Nodes that connect their group to others usually
  end up with high network metrics. Boundary
  spanners such as Fernando, Garth, and Heather are
  more central than their immediate neighbors whose
  connections are only local, within their
  immediate cluster
• Boundary spanners are well-positioned to be
  innovators, since they have access to ideas and
  information flowing in other clusters. They are
  in a position to combine different ideas and
  knowledge, found in various places, into new
  products and services

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Peripheral Players

• Most people would view the nodes on the periphery
  of a network as not being very important. In
  fact, Ike and Jane receive very low centrality
  scores for this network. Yet, peripheral nodes
  are often connected to networks that are not
  currently mapped. Ike and Jane may be contractors
  or vendors that have their own network outside of
  the company -- making them very important
  resources for fresh information not available
  inside the organization!

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Recent Applications of SNA

• Help large organization locate employees in new
  buildings
• Examine a network of farm animals to analyze how
  disease spreads from one cow to another
• Map network of Jazz musicians based on musical
  styles and CD sales
• Discover emergent communities of interest amongst
  faculty at various universities
• Reveal cross-border knowledge flows based on
  research publications
• Expose business ties financial flows to
  investigate possible criminal behavior
• Uncover network of characters in a fictional work
  
• Analyze managers networks for succession
  planning
• Locate technical experts and the paths to access
  them in engineering organization
• Disaster response networks

• Build a grass roots political campaign
• Determine influential journalists and analysts in
  the IT industry
• Unmask the spread of HIV in a prison system
• Map executives personal network based on email
  flows
• Discover the network of Innovators in a regional
  economy
• Analyze book selling patterns to position a new
  book
• Map a group of entrepreneurs in a specific
  marketplace
• Map interactions amongst blogs on various topics
• Reveal key players in an investigative news story
  
• Map national network of professionals involved in
  a change effort
• Improve the functioning of various project teams
• Map communities of expertise in various medical
  fields

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Meta-Matrix Network Framework
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SNA Software (UCINET)

• UCINET is a comprehensive software program for
  the analysis of social networks
• The program contains several network analytic
  routines (e.g., centrality measures, dyadic
  cohesion measures, positional analysis
  algorithms, and clique etc.), and general
  statistical and multivariate analysis tools such
  as multidimensional scaling, correspondence
  analysis, factor analysis, cluster analysis, and
  multiple regression
• Available online www.analytictech.com/ucinet.htm

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Org-chart shows how authority ties should look
SOURCE Brandes, Raab and Wagner (2001)
lthttp//www.inf.uni-konstanz.de/brandes/publicat
ions/brw-envsd-01.pdfgt
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but the digraph of actual advice-seeking
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can be restructured to reveal the real
hierarchy!
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Nonprofit Governance

• Organizational Chart
• Formal organizational structure
• Friendship Networks
• Strengths of weak ties
• Advice Networks
• Structural holes

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Friendship Network (Nonprofit)
? Board member, ? Staff
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Advice Network (Nonprofit)
? Board member, ? Staff
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Questions for Communities Based on Network
Analysis

• Which community agencies or groups are most (and
  least) central in the network, and are these
  agencies or groups essential for addressing
  community needs in a particular problem area?
• Which core network members have links to
  important resources through their involvement
  with organizations outside the network that might
  benefit other network members?
• Are the critical ties among agencies in the
  community based solely on personal relationships,
  or have these ties become formalized so that they
  are sustainable over time?
• Are the relationships among agencies in the
  network strong or weak? If they are weak, should
  these relationships be maintained as is, or
  should they be strengthened?
• Which groups of organizations within the network
  currently have strong working relationships? How
  can these groups be mobilized to meet the broader
  objectives of the network?
• Based on comparative network data over time, has
  reasonable progress been made in building
  community capacity through developing stronger
  network ties?
• What is the level of trust among agencies working
  together, and has it increased or decreased over
  time? If it has declined, how can it be
  strengthened?
• What are the benefits and drawbacks of
  collaboration, have these changed over time, and
  how can benefits be enhanced and drawbacks
  minimized?

Source Provan, K. G. et al. 2005
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Central Florida Nonprofit Network
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Conclusion

• Information generated through SNA will have
  practical value if it effectively presented,
  discussed, accepted, and acted on by network
  participants
• SNA can be a valuable tool for helping community
  leaders (network members) to understand network
  structure and processes

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References

• Provan, K. G., M. A. Veazie, L. K. Staten, N. I.
  Teufel-Shone . 2005. The Use of Network Analysis
  to Strengthen Community Partnerships, Public
  Administration Review. Volume 65(5) 603 - 613.
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• Knoke Kuklinski, Network Analysis, 1983, Sage
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• Wellman Berkowitz, eds., Social Structures,
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• David Knoke, Political Networks, 1990
• John Scott, Social Network Analysis, 1991
• Ron Burt, Structural Holes, 1992
• Manuel Castells, The Rise of Network Society,
  2000
• Wasserman Faust, Social Network Analysis, 1992
• Nan Lin, Social Capital (monograph reader), 2001

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Discussions Questions

• Thank you