What are social networks?
• The set of (exchange) relationships
between people or other social units.
• A directed graph, with people, groups,
or organizations as the nodes and the
things exchanged as the link
• Vary in size, density, clumpiness
• Some types of networks
• Communication
• Advice/information
• Friendship
• Trust/social support
• Tangible exchange/Material support
• Similarity
• Structure matters
•Clique
•Isolates
•Stars
•Boundary spanners
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Why are they important?
• Examining social networks can help diagnose
organizational problems
• Find informational bottlenecks/distribution channels
• Select successful team leaders and managers
• Good managers understand that there are both formal and
informal networks in an organization
• Source of social capital, with benefits to individuals and
organizations
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Communication Flow in an R&D Lab
(Where do you get technical info?)
Allen, T. (1977). Managing the flow of technology. Cambridge, MA: MIT Press.
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Race & school friendships
Moody, Jame (2002) Race, School Integration, and Friendship Segregation in America. The
American journal of sociology [0002-9602] Moody yr:2002 vol:107 iss:3 pg:679
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Familiarity in a CMU Project Class
79% nonAsian
83%
Asian
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Stunning Density Comparison
Architecture
BHA/BSA:
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Bloggers X Party
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Types of Relationships Among People
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Why are they important?
• Actors who are connected, influence each other
• “Goods” (e.g., info, opportunities, power) flow through
networks
• Actors’ position in the network influence their success
• Good managers/researchers cultivate extensive networks,
inside & outside the organization at all levels
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Social Capital
• Investment in time, energy and other
resources in individual and organized social
relationships
• Relationships have benefits
• Knowledge, innovation, resources
• Individual health and happiness
• Community efficiency, safety and quality
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Social Capital
• One definition (Bourdieu, 1992: p. 119)
• The sum of the resources, actual or virtual, that accrue to an
individual or a group by virtue of possessing a durable network
of more or less institutionalized relationships of mutual
acquaintance and recognition
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Social Capital
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Social Support  Health & Happiness
• Age-adjusted relative risk of
dying among those lacking
social contact during a 9-year
period (Berkman, 1983)
• Sources of social support
• Being married
• Frequent contact with family
and close friends
• Active member of a church
• Active participation in a club
or other social group
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Strength of ties
• Strong ties (Krackhard)
• Intimacy, self-disclosure, provide support
• Feel close w/frequent contact
• Spouse, relatives, close friends
• Weak ties (Granovetter)
• Diverse resources, broader base
• Feel distance w/infrequent contact
• Acquaintances, colleagues from elsewhere
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Nature of the Social Tie Matters
• Strong tie = “close relationship/friend”
• Social relationship with high frequency, emotional
commitment, multiplicity (overlap), and reciprocity
• Strong ties tend to know same things & people
• Strong ties tend to fill in the gaps (e.g., friends of
friends become friends; friends tend to share taste)
• Strong ties useful for
•
•
•
•
Money
Advice
Arduous help
Friendship
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Nature of the Social Tie Matters
• Weak tie = “weak relationship/causal acquaintance”
• Social relationships with low frequency, intensity, breadth, and
reciprocity (Granovetter: Strength of Weak Ties)
• Hypothesis: Weak ties lead to more extensive and diverse social
networks, and are more likely to overcome gaps of class, race, and
other sources of division
• Data: Job changers get their jobs through weak ties:
e.g.. only 16% from contacts they see weekly and 28%
they see less than yearly
• Weak ties useful for
• New information
• Finding jobs
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Experiment on how info flows online
• ~ 400M Facebook users either had a status update with
a URL randomly deleted from their feed or not
URL retained: With link in feed
URL deleted: No link in feed
• DV: Probability of friend posting the link
Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information
diffusion Proceedings of the 21st international conference on World Wide Web. (pp. 519-528). 17
Feed subjects share more
• Subjects in feed condition were more likely to
share
• Feed: .191% shared
• No feed: .025% shared
• Increase = 7.37x
• Shared fasted
• More exposure leads to
more sharing
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Strong are influential, but weak ties
expose friends to new info
• Strong Ties Are More Influential per capita
• But effect of friend receiving the feed declines
with tie strength  stronger ties are getting the
link from other sources
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Weak ties were more influential than
strong ties in the aggregate
• A single strong ties is
more influential than a
single weak one
• But people have many
more weak ties than
strong ones
 In aggregate, weak ties
are more important
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Basic Concepts
Networks
 Tie Strength
Key Players
Cohesion
CNM Social 21
Media
Module – Giorgos
How to represent various social networks
How to identify strong/weak ties in the network
How to identify key/central nodes in network
Measures of overall network structure
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Edge weights as relationship strength
•
•
Edges can represent interactions, flows
of information or goods,
similarities/affiliations, or social relations
Specifically for social relations, a ‘proxy’
for the strength of a tie can be:
(a) the frequency of interaction (communication) or
the amount of flow (exchange)
(b) reciprocity in interaction or flow
(c) the type of interaction or flow between the two
parties (e.g., intimate or not)
(d) other attributes of the nodes or ties (e.g., kin
relationships)
(e) The structure of the nodes’ neighborhood (e.g.
many mutual ‘friends’)
•
Surveys and interviews allows us to
establish the existence of mutual or onesided strength/affection with greater
certainty, but proxies above are also
useful
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Basic Concepts
Networks
Tie Strength
 Key Players
Cohesion
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How to represent various social networks
How to identify strong/weak ties in the network
How to identify key/central nodes in network
Measures of overall network structure
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Measures of Centrality
Centrality measure
Interpretation in social networks

Degree
How many people can this person reach directly?

Betweenness
How likely is this person to be the most direct route
between two people in the network?

Closeness
How fast can this person reach everyone in the
network?
Eigenvector
How well is this person connected to other wellconnected people?

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Degree centrality
•
•
•
•
A node’s (in-) or (out-)degree is the
number of links that lead into or out
of the node
In an undirected graph they are of
course identical
Often used as measure of a node’s
degree of connectedness and hence
also influence and/or popularity
Useful in assessing which nodes are
central with respect to spreading
information and influencing others in
their immediate ‘neighborhood’
Hypothetical graph
2
1
4
1
2
3
5
4
1
3
6
4
7
1
Nodes 3 and 5 have the highest degree
(4)
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Betweenness centrality
 For a given node v, calculate the
number of shortest paths between
nodes i and j that pass through v,
and divide by all shortest paths
between nodes i and j
 Sum the above values for all node
pairs i,j
 Sometimes normalized such that the
highest value is 1or that the sum of
all betweenness centralities in the
network is 1
 Shows which nodes are more likely
to be in communication paths
between other nodes
 Also useful in determining points
where the network would break apart
(think who would be cut off if nodes 3
or 5 would disappear)
Node 5 has higher betweenness centrality
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than 3
0
1
6.5
0
2
3
5
4
0
1.5
6
9
7
0
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Eigenvector centrality
•
•
•
•
A node’s eigenvector centrality is
proportional to the sum of the
eigenvector centralities of all
nodes directly connected to it
In other words, a node with a high
eigenvector centrality is
connected to other nodes with
high eigenvector centrality
This is similar to how Google
ranks web pages: links from
highly linked-to pages count more
Useful in determining who is
connected to the most connected
nodes
Node 3 has the highest eigenvector centrality,
closely followed by 2 and 5
0.36
1
2
0.49
0.54 3
0.19
5
4
0.17
6
0.49
7
0.17
Note: The term ‘eigenvector’ comes from mathematics (matrix
algebra), but it is not necessary for understanding how to interpret this
measure
Values computed with the sna package in the R programming environment. Definitions of centrality measures may vary slightly in other software.
CNM Social 27
Media
Module – Giorgos
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Basic Concepts
Networks
Tie Strength
Key Players
 Cohesion
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How to represent various social networks
How to identify strong/weak ties in the network
How to identify key/central nodes in network
How to characterize a network’s structure
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Density
•
•
•
•
•
A network’s density is the ratio of the number of
edges in the network over the total number of
possible edges between all pairs of nodes
(which is n(n-1)/2, where n is the number of
vertices, for an undirected graph)
In the example network to the right
density=5/6=0.83 (i.e. it is a fairly dense
network; opposite would be a sparse network)
It is a common measure of how well connected
a network is (in other words, how closely knit it
is) – a perfectly connected network is called a
clique and has density=1
A directed graph will have half the density of its
undirected equivalent, because there are twice
as many possible edges, i.e. n(n-1)
Density is useful in comparing networks against
each other, or in doing the same for different
regions within a single network
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Edge present in network
Possible but not present
1
2
3
4
density = 5/6 = 0.83
1
2
3
4
density = 5/12 = 0.42
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Dense Social Networks Are Good
for the Group (Coleman, 1990)
• Dense networks are useful at the
organizational level
• Provide
• Information & other resources
• Trust thru effects of reputation
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Who Helps Whom with the Rice
Harvest?
Which Village Is More Likely to Survive?
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Groups rely upon networks for success
• Allen: Bring technical knowledge into R&D teams
• Coleman: Rapid adoption of medical innovations
among community of MDs
• Curtis: Software engineering
• Getting application domain requirements
• Keeping up with changing environment of use and
development
• Ancona: New product development teams
• Convince the boss
• Get the support of "sister" departments
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Allen: Gatekeepers
• Gatekeepers moderate the flow of technical
information into R&D groups
• Connected both within and outside the group
• Technically competent & often a supervisor
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Structural Holes
• Network closure – relations are embedded in
a network
• Enhance group identification
• Foster exchange of ideas
• Structural holes – relations bridge
disconnected networks
• Access to unique information
• Broker third parties
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Structural holes – benefits
• A structural hole exists when there is only a
weak connection between two dense clusters
• Control benefits:
• brokers control the interaction between two network
components
• Information benefits:
• brokers have access to unique information, this makes them
invaluable
• Structural holes are a competitive advantage
• Separate non-redundant sources of information
• Information from different sources is more additive
than overlapping
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Advantages of Structural Holes (Burt, 2000)
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