Social Network Theory

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Social Network Theory

• Applications to Supply Networks

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• A social network is a social structure made of
  nodes which are generally individuals or
  organizations. It indicates the ways in which
  they are connected through various social
  familiarities ranging from casual acquaintance to
  close familial bonds. The term was first coined
  in 1954 by J. A. Barnes (in Class and Committees
  in a Norwegian Island Parish, "Human Relations").
  The maximum size of social networks tends to be
  around 150 people and the average size around 124
  (Hill and Dunbar, 2002).

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• Social network analysis (also sometimes called
  network theory) has emerged as a key technique in
  modern sociology, anthropology, Social Psychology
  and organizational studies, as well as a popular
  topic of speculation and study. Research in a
  number of academic fields have demonstrated that
  social networks operate on many levels, from
  families up to the level of nations, and play a
  critical role in determining the way problems are
  solved, organizations are run, and the degree to
  which individuals succeed in achieving their
  goals.

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• The shape of the social network helps determine a
  network's usefulness to its individuals. Smaller,
  tighter networks can be less useful to their
  members than networks with lots of loose
  connections (weak ties) to individuals outside
  the main network. More "open" networks, with many
  weak ties and social connections, are more likely
  to introduce new ideas and opportunities to their
  members than closed networks with many redundant
  ties. In other words, a group of friends who only
  do things with each other already share the same
  knowledge and opportunities. A group of
  individuals with connections to other social
  worlds is likely to have access to a wider range
  of information. It is better for individual
  success to have connections to a variety of
  networks rather than many connections within a
  single network. Similarly, individuals can
  exercise influence or act as brokers within their
  social networks by bridging two networks that are
  not directly linked (called filling social holes).

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• Social networks have also been used to examine
  how companies interact with each other,
  characterizing the many informal connections that
  link executives together, as well as associations
  and connections between individual employees at
  different companies. These networks provide ways
  for companies to gather information, deter
  competition, and even collude in setting prices
  or policies.

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• Power within organizations, for example, has been
  found to come more from the degree to which an
  individual within a network is at the center of
  many relationships than actual job title. Social
  networks also play a key role in hiring, in
  business success for firms, and in job
  performance.
• Diffusion of innovations theory explores social
  networks and their role in influencing the spread
  of new ideas and practices. Change agents and
  opinion leaders often play major roles in
  spurring the adoption of innovations, although
  factors inherent to the innovations also play a
  role.

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• The so-called rule of 150, states that the size
  of a genuine social network is limited to about
  150 members (sometimes called Dunbar's number).
  The rule arises from cross-cultural studies in
  sociology and especially anthropology of the
  maximum size of a village (in modern parlance
  most reasonably understood as an ecovillage). It
  is theorized in evolutionary psychology that the
  number may be some kind of limit of average human
  ability to recognize members and track emotional
  facts about all members of a group.

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• Degrees of Separation and the Global Social
  Network
• The small world phenomenon is the hypothesis that
  the chain of social acquaintances required to
  connect one arbitrary person to another arbitrary
  person anywhere in the world is generally short.
  The concept gave rise to the famous phrase six
  degrees of separation after a 1967 small world
  experiment by psychologist Stanley Milgram which
  found that two random US citizens were connected
  by at most, six acquaintances. Current internet
  experiments continue to explore this phenomenon,
  including the Ohio State Electronic Small World
  Project and Columbia's Small World Project. As of
  2005, these experiments confirm that about five
  to seven degrees of separation are sufficient for
  connecting any two people through the internet.

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Indices for Social Network Analysis

• Betweenness 
• Degree an individual lies between other
  individuals in the network the extent to which a
  node is directly connected only to those other
  nodes that are not directly connected to each
  other an intermediary liaisons bridges.
  Therefore, it's the number of people who a person
  is connected to indirectly through their direct
  links. See also Betweenness
• Closeness  
• The degree an individual is near all other
  individuals in a network (directly or
  indirectly). It reflects the ability to access
  information through the "grapevine" of network
  members. Thus, closeness is the inverse of the
  sum of the shortest distances between each
  individual and every other person in the network.
  See also Closeness
• Degree 
• The count of the number of ties to other actors
  in the network. See also degree (graph theory)

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Indices for Social Network Analysis

• Eigenvector Centrality 
• Eigenvector centrality is a measure of the
  importance of a node in a network. It assigns
  relative scores to all nodes in the network based
  on the principle that connections to nodes having
  a high score contribute more to the score of the
  node in question.
• Clustering Coefficient 
• The clustering coefficient is a measure of the
  likelihood that two associates of a node are
  associates themselves. A higher clustering
  coefficient indicates a greater 'cliquishness'.
• Cohesion 
• Refers to the degree to which actors are
  connected directly to each other by cohesive
  bonds. Groups are identified as cliques if
  every actor is directly tied to every other
  actor, or social circles if there is less
  stringency of direct contact

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Indices for Social Network Analysis

• Constraint  Contagion Density 
• Individual-level density is the degree a
  respondent's ties know one another/ proportion of
  ties among an individual's nominees. Network or
  global-level density is the proportion of ties in
  a network relative to the total number possible
  (sparse versus dense networks).
• Integration 
• Group degree centralisation
• A measure of group dispersion or how network
  links focus on a specific node or nodes.
• Radiality 
• Degree an individuals network reaches out into
  the network and provides novel information and
  influence

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Indices for Social Network Analysis

• Reach 
• The degree any member of a network can reach
  other members of the network. See also reach.
• Structural Equivalence 
• Refers to the extent to which actors have a
  common set of linkages to other actors in the
  system. The actors dont need to have any ties to
  each other to be structurally equivalent.
• Structural Hole 
• Static holes that can be strategically filled by
  connecting one or more links to link together
  other points. Linked to ideas of social capital
  if you link to two people who are not linked you
  can control their communication.

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• What is social capital Social capital is
  generally referred to as the set of trust,
  institutions, social norms, social networks, and
  organizations that shape the interactions of
  actors within a society and are an asset for the
  individual and collective production of
  well-being. At the macro level, social capital
  can affect the economic performance and the
  processes of economic growth and development.

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SNT Measures

• The oldest, and also simplest notion referring to
  quantitative aspects of social capital its volume
  or extensity. The (often implicit) theoretical
  argument is that bigger, larger, or simply more
  social capital is better social capital for
  individual goal attainment (Bourdieu, 1980 Burt,
  1992), without specifically referring to (numbers
  of) relationships, resources, or the availability
  of any resources.
• A second, more often used notion is that of
  diversity because specific resources and
  relationships can be located and accessed more
  successfully when more differentiation is present
  in the network, this results in better social
  capital. More specifically, this notion has been
  applied to either the diversity of social
  resource collections (Erickson, 1996 Lin, 2001a)
  or the diversity of network relationships, as
  worded in hypotheses considering the presence of
  weak ties (Granovetter, 1973), structural holes
  (Burt, 1992), and other many other typical
  configurations in social network structures
  (Borgatti et al, 1998).

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SNT Measures

• A third class of morphological social capital
  characteristics that could be considered for
  measurement is based on specific resources
  present in networks.
• The only social capital measure that has been
  used regularly in this fashion is highest
  accessed prestige' from the Position Generator
  model (Lin Dumin, 1986 Lin, Fu, and Hsung,
  2001), based on the hypothesis that positive
  social capital results from accessing network
  members with high prestige (we will return to
  this model shortly). Identifying more of these
  specific (groups of) resources is one of the
  current aims of social capital research.

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SNT Measures

• The most comprehensive measurement instrument
  used to construct social capital measures is the
  exchange type Name Generator / interpreter
  (McCallister Fischer, 1978). This method maps
  the ego-centered social network as a starting
  point for a subsequent social resource inventory.
  It can result in very detailed and informative
  social capital descriptions, both in terms of
  relationships and resources. The single
  core'-network identifying name generating item
  with whom do you talk about personal matters'
  stems from this approach, and has been widely
  used ever since (e.g. in the American General
  Social Survey, see Marsden, 1987).
• A measurement method focusing more on the
  presence of social resources than relationships
  in networks is the Position Generator (Lin
  Dumin, 1986 Lin, Fu, and Hsung, 2001). This
  method measures access through network members to
  certain occupations, that represent social
  resource collections based on job prestige in an
  hierarchically modeled society, following Lin's
  theories of social resources and social capital
  (Lin, 1982 2001a). This instrument is more
  interview-friendly, and measures calculated from
  it are firmly rooted in theory.
• Another more resource-oriented social capital
  measurement instrument is the Resource Generator
  (Snijders, 1999 Van der Gaag Snijders, 2003b).
  This instrument asks about access to a fixed list
  of specific social resources, that each represent
  a vivid, concrete sub collection of social
  capital, together covering several domains of
  life. This instrument can be administered
  quickly, and result in easily interpretable
  representations of social capital, with more
  possibilities for use in goal specificity
  research.

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• In a recent network design book, Advanced IP
  Network Design, the authors define a
  well-designed topology as the basis of a
  well-behaved and stable network. They propose the
  idea that, three competing goals must be
  balanced for good network design reducing hop
  count, reducing available paths, and increasing
  the number of failures the network can withstand
  7. Social network algorithms can assist in
  meeting all three of these goals. Reducing the
  hop count infers minimizing the average path
  length throughout the network. This can be done
  by maximizing the closeness of all nodes to each
  other. Reducing the available paths leads to
  minimizing the number of shortest paths between
  members in the network. Increasing the number of
  failures a network can withstand focuses on
  minimizing the centralization of the entire
  network. Social network models can model our
  computer networks and suggest link changes to
  form an effective topology that has a short
  average hop count, not too many paths, and just
  enough redundancy.

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• The leading hypothesis is that as social
  diversity increases, the level of exposure to a
  certain illness also increases. Thus the immune
  system is better prepared to defend itself
  against any future exposure to the sickness.
  However, the researchers have so far not been
  able to thoroughly support this hypothesis
  experimentally. What this research does show is
  another strong benefit of having high social
  diversity or social capital 8.
•             The results found by these
  researchers are quite surprising, The magnitude
  of the health risk of being relatively isolated
  (socially) is comparable to the risks associated
  with cigarette smoking, high blood pressure and
  obesity and is robust even after controlling for
  these and other traditional risk factors 8. It
  appears that cultural isolation can have a
  profound effect on physical well being. Their
  research has also shown that the development of
  mental illness is associated with the level of
  social contact a person has. Some researchers
  believe that this is due to the fact that
  peoples identities are tied to their social
  roles. By meeting role expectations, individuals
  are given the opportunity to enhance their
  self-esteem. They believe that these social roles
  provide a purpose to life. They imply that a
  sense of purpose is an integral component of
  psychological well being.

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• Research in social networks has also proven to
  provide great benefits to the field of marketing.
  Social networks and their patterns of
  relationships are a fundamental fact of market
  behavior and can be used effectively as a basis
  for marketing strategies. A major challenge
  facing marketing strategists is how to increase
  the effectiveness of social network based
  marketing strategies. In order to reach this goal
  marketing researchers and scientists have
  collected social network related data and have
  analyzed it using social network analysis. The
  study of social networks is beginning to be
  widely used in marketing. One of the reasons why
  it has taken so long to have an impact is because
  of the scarcity and difficulty in obtaining the
  requisite data.

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•             Research in social network analysis
  is being performed by government agencies for use
  in defense programs. The Total Information
  Awareness program sponsored by the Defense
  Department is currently working on a project
  known as Scalable Social Network Analysis (SSNA).
  SSNA aims to model networks of connections like
  social interactions, financial transactions,
  telephone calls, and organizational memberships
  13. They are attempting to model the social
  networks that terrorists belong to. The purpose
  of the SSNA algorithms program is to extend
  techniques of social network analysis to assist
  with distinguishing potential terrorist cells
  from legitimate groups of people, based on their
  patterns of interactions, and to identify when a
  terrorist group plans to execute an attack. This
  is an extremely ambitious project considering the
  scale of the social networks that these
  researchers are attempting to model. In order to
  be successful SSNA will require information on
  the social interactions of the majority of people
  around the globe. Since the Defense Department
  cannot easily distinguish between peaceful
  citizens and terrorists, it will be necessary for
  them to gather data on innocent civilians as well
  as on potential terrorists.