Transcript Social Network Theory

Social Network Theory
Applications to Supply Networks
• 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).
• 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.
Social network theory views social relationships in terms of nodes and ties. Nodes are the
individual actors within the networks, and ties are the relationships between the actors.
There can be many kinds of ties between the nodes. In its most simple form, a social
network is a map of all of the relevant ties between the nodes being studied. The network
can also be used to determine the social capital of individual actors. These concepts are
often displayed in a social network diagram, where nodes are the points and ties are the
lines.
• 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).
• 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.
• 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.
• 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.
• 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.
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)
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
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 individual’s network reaches out into the
network and provides novel information and influence
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
don’t 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.
• 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.
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).
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.
SNT Measures
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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.
• In a recent network design book, Advanced IP Network Design, the
authors define a well-designed topology as the basis of a wellbehaved 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 people’s identities are tied to their social roles. By meeting role
expectations, individuals are given the opportunity to enhance their selfesteem. 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.
• 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.