Noshir Contractor

1
Critical Review of Social Network Theories and
Models
Noshir Contractor Professor, Departments of
Speech Communication Psychology Director, Age
of Networks Initiative, Center for Advanced
Study Director, Science of Networks in
Communities - National Center for Supercomputing
Applications University of Illinois at
Urbana-Champaign nosh_at_uiuc.edu
2

• Turn on power set MODE with MODE button. You
  can confirm the MODE you chose as the red
  indicator blinks.
• Lamp blinks when (someone with) a Lovegety for
  the opposite sex set under the same MODE as yours
  comes near.
• FIND lamp blinks when (someone with) a Lovegety
  for the opposite sex set under different mode
  from yours comes near. May try the other MODES to
  GET tuned with (him/her) if you like.

3
Social Petworking Reported in Wired, April
11, 2005
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Aphorisms about Networks

• Social Networks
• Its not what you know, its who you know.
• Cognitive Social Networks
• Its not who you know, its who they think you
  know.
• Knowledge Networks
• Its not who you know, its what they think you
  know.

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Cognitive Knowledge Networks
Source Newsweek, December 2000
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INTERACTION NETWORKS
Non Human Agent to Non Human Agent Communication
Non Human Agent (webbots, avatars, databases,
push technologies) To Human Agent
Publishing to knowledge repository
Retrieving from knowledge repository
Human Agent to Human Agent Communication
Source Contractor, 2001
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COGNITIVE KNOWLEDGE NETWORKS
Non Human Agents Perception of Resources in a
Non Human Agent
Human Agents Perception of Provision of
Resources in a Non Human Agent
Non Human Agents Perception of what a Human
Agent knows

Human Agents Perception of What Another Human
Agent Knows
Why Amazon thinks I am pregnant and Tivo
thinks I am gay .
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Human to Human Interactions and Perceptions
Human to Non Human Interactions and Perceptions
Non Human to Human Interactions and Perceptions
Non Human to Non Human Interactions and
Perceptions
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WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND
RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE
NETWORKS?
10
Monge, P. R. Contractor, N. S. (2003).
Theories of Communication Networks. New York
Oxford University Press.
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Why do actors create, maintain, dissolve, and
reconstitute network links?

• Theories of self-interest
• Theories of social and resource exchange
• Theories of mutual interest and collective action

• Theories of contagion
• Theories of balance
• Theories of homophily
• Theories of proximity
• Theories of co-evolution

• Sources
• Monge, P. R. Contractor, N. S. (2003).
  Theories of Communication Networks. New York
  Oxford University Press.
• Contractor, N. S., Wasserman, S. Faust, K.
  (in press). Testing multi-theoretical multilevel
  hypotheses about organizational networks An
  analytic framework and empirical example. Academy
  of Management Review.

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Co-evolution of knowledge networks and 21st
century organizational forms

• NSF KDI Initiative 1999-05. PI Noshir
  Contractor, University of Illinois.
• Co-P.I.s Monge, Fulk, Bar (USC), Levitt, Kunz
  (Stanford), Carley (CMU), Wasserman (Indiana),
  Hollingshead (Illinois).
• Three dozen industry partners (global, profit,
  non-profit)
• Boeing, 3M, NASA, Fiat, U.S. Army, American Bar
  Association, European Union Project Team, Pew
  Internet Project, etc.

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• Public Goods / Transactive Memory
• Allocation to the Intranet
• Retrieval from the Intranet
• Perceived Quality and Quantity of Contribution to
  the Intranet

• Transactive Memory
• Perception of Others Knowledge
• Communication to Allocate Information

Communication to Retrieve Information

• Inertia Components
• Collaboration
• Co-authorship
• Communication

Social Exchange - Retrieval by coworkers on
other topics
Proximity -Work in the same location
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Motivation for Information Retrieval in
Knowledge Networks
1. Social Communication 0.144 2. Perception
of Knowledge Communication to
Allocate 0.995 3. Perception of Knowledge
Provision 0.972 4. Perception of Knowledge,
Social Exchange, Social Communication 0.851
5. Perception of Knowledge, Proximity,
Social Communication 0.882
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Enter Cyberinfrastructure, Web 2.0, and Social
Computing .

• Until the advent of the Web browser we used
  relatively non-extensible technologies (like
  WAIS, Archie, Gopher) for retrieving from the
  Internet.
• Cyberinfrastructure, Web 2.0, and Social
  Computing technologies can do for building and
  sustaining community what the Web browser did for
  retrieving information from the Internet.

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From Mosaic to Cyberinfrastructure

• Mosaic
• By early 1990s, the internet had a wealth of
  resources, but they were inaccessible to most
  people
• Mosaic facilitated the access of the Internet by
  all
• Cyberinfrastructure
• Cyberinfrastructure will facilitate the seamless
  and interconnected use of all the digital
  resources on the Internet (datasets, documents,
  sensors, analytics, computing, and communication)

For more information on NCSAs history, see
http//www.ncsa.uiuc.edu/20years/
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Science and Engineering Cyberinfrastructures
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Multidimensional Networks Multiple Types of
Nodes and Multiple Types of Relationships
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Its all about Relational Metadata

• Just as Wikipedia is an empty shells for content,
  we are seeing the development of global
  conventions for development of an empty shell for
  relational metadata (Borner, 2005)
• Technologies that capture communities
  meta-data (Pingback and trackback in interblog
  networks, blogrolls, data provenance)
• Technologies to tag communities relational
  metadata (from Dublin Core taxonomies to
  folksonomies (wisdom of crowds) like
• Tagging pictures (Flickr)
• Social bookmarking (del.icio.us, LookupThis,
  BlinkList, Connectedy)
• Social citations (CiteULike.org)
• Social libraries (discogs.com, LibraryThing.com)
• Social shopping (SwagRoll, Kaboodle,
  thethingsiwant.com)
• Social networks (FOAF, XFN, MySpace, Facebook)
• Technologies to manifest communities
  relational metadata (Tagclouds, Recommender
  systems, Rating/Reputation systems, Social
  Networking services, search engines, ISIs
  HistCite, Network visualization services)

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Projects on Enabling Communities with
Cyberinfrastructure

• Digital Government Communities
• Environment Engineering (NSF)
• Public Health (NSF, NIH, CDC, American Legacy)
• Emergency Response (NSF)
• World of Warcraft, UIUC Cross-Campus Initiative
• PackEdge Community of Practice, Procter Gamble
• Transnational Immigrant Communities, Rockefeller
  Foundation
• Economic Resilience Community, Rockefeller
  Foundation

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Enabling Disaster Response Communities with
Cyberinfrastructure

• CLEANER
• Collaborative Large-scale Engineering Analysis
  Network for Environmental Research
• Human-dominated, complex environmental systems,
  e.g.,
• River basins
• Coastal margins
• What researchers requested
• Access to live and archived
• sensor data
• Analyze, visualize and compare
• data
• Link to computational models
• Collaborate with colleagues
• Organize, automate and share cyber-research
  processes

Users can simultaneously view and discuss data
and analyses
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Projects on Enabling Communities with
Cyberinfrastructure

• Digital Government Communities
• Environment Engineering (NSF)
• Public Health (NSF, NIH, CDC, American Legacy)
• Emergency Response (NSF)
• World of Warcraft, UIUC Cross-Campus Initiative
• PackEdge Community of Practice, Procter Gamble
• Transnational Immigrant Communities, Rockefeller
  Foundation
• Economic Resilience Community, Rockefeller
  Foundation

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Tobacco Surveillance, Epidemiology, and
Evaluation Network (TSEEN)

• National Cancer Institute
• Center for Disease Controls National Center for
  Health Statistics (NCHS),
• Center for Disease Controls Office of Smoking
  and Health (OSHO,
• Agency for Healthcare Research and Quality
  (AHRQ),
• National Library of Medicine (NLM) and
• Non-government agencies such as the American
  Legacy Foundation.

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TSEEN Network Referral System

• Low-tar cigarettes cause more cancer than regular
  cigarettes
• A pressing need for systems that will help the
  TSEEN members effectively connect with other
  individuals, data sets, analytic tools,
  instruments, sensors, documents, related to key
  concepts and issues

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Projects on Enabling Communities with
Cyberinfrastructure

• Digital Government Communities
• Environment Engineering (NSF)
• Public Health (NSF, NIH, CDC, American Legacy)
• Emergency Response (NSF)
• World of Warcraft, UIUC Cross-Campus Initiative
• PackEdge Community of Practice, Procter Gamble
• Transnational Immigrant Communities, Rockefeller
  Foundation
• Economic Resilience Community, Rockefeller
  Foundation

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CP2R ICT Support in Emergency Management Networks

Drawing Analogies from Natural Systems
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Natural Communities Ants Honey Bees
ENTOMOLOGY Learning from natural robust
societies.
Successful systems (evolution time) Ant - based
models have successfully been applied to solve
optimization Dorigo, 1996 Botee, 1999 and
networking Bonabeau, 2000 problems, among
others.
Bees setting and objectives in foraging Seeley,
et al. 1991 resembles disaster relief response
scenario (collective decision-making).
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Problem Information Overload

• Hundreds or Thousands of first responders operate
  sharing couple of voice channels (radio,
  cell-phones) Domel, 2001

http//www.hollandsentinel.com/images/031503/Borcu
lofire4.jpg

• If technology provides a mean to enhance delivery
  and media of information, we envision this
  problem would increase

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Information Overload Ants
Analogy (Ants alarm propagation)
Division of Labor each ant has a threshold for
each stimulus (pheromone). When stimulus is
greater than threshold the ant will be on alarm
mode. Centels ants detects a hazard and release
alarm pheromone (volatile). Each pheromone
release will last for a limited time seconds or
minutes. The heterogeneous response to alarm
pheromone avoids all ants react immediately (good
or bad?).
HOW
Idea Actors will propagate information received
only if the stimulus, i.e., quality of
information, is greater than his/her threshold
for that type of information. Avoiding cascading
effect controlling information overload.
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Natural Communities Honey Bees
At hive unloading
At hive unloading
nectar from A
nectar from B
(H
)
(H
)
Honey Bees (Apis melifera)
A
B
Foraging Model Seeley, 1991
p1
p7
p5
p3
f
A
f
B
x
x
1-f
A
1-f
B
x
x
Following
f
A(1-f
A)
other dances
f
B(1-f
B)
d
x
d
x
(F)
Dancing for A
Dancing for B
(D
)
(D
)
A
B
(1-f
A)(1-f
A)
(1-f
B)(1-f
B)
d
x
d
x
p4
p6
p2
The system evaluates ALL the information, though
individuals evaluate only partial information
f
A
f
B
f
f
Foraging at nectar
Foraging at nectar
source A
source B
(A)
(B)
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Projects on Enabling Communities with
Cyberinfrastructure

• Digital Government Communities
• Environment Engineering (NSF)
• Public Health (NSF, NIH, CDC, American Legacy)
• Emergency Response (NSF)
• World of Warcraft, UIUC Cross-Campus Initiative
• PackEdge Community of Practice, Procter Gamble
• Transnational Immigrant Communities, Rockefeller
  Foundation
• Economic Resilience Community, Rockefeller
  Foundation

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Rise of WoW
Source http//www.mmogchart.com/
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Expertise/Information Retrieval Time One
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Expertise/Information Retrieval Time Two
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Expertise/Information Retrieval Time Three
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Results

• Incentive for creating a WoW link with someone
• -1.55 (cost of creating a link)
• 0.55 (benefit of reciprocating)
• 0.89 (benefit for being a friend of a friend)
• 0.04 (benefit of connecting to an expert)

All coefficients significant at 0.05 level
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3D Strategy for Enhancing Networks

• Discovery Effectively and efficiently foster
  network links from people to other people,
  knowledge, and artifacts (data sets/streams,
  analytic tools, visualization tools, documents,
  etc.). If only we knew what we knew.
• Diagnosis Assess the health of networks - in
  terms of scanning, absorptive capacity,
  diffusion, robustness, and vulnerability to
  external environment
• Design or re-wire networks using social and
  organizational incentives (based on social
  network research) and network referral systems to
  enhance evolving and mature communities.

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Discovery - IKNOW Demo
http//iknow.spcomm.uiuc.edu Use courtesy logins
and passwords provided on the website
45
Diagnosis - Scanning
Scanning from many sources (such as countries)
US

US
NL
Rest of Network
US
IT
BE

US
IT
BE
BE
Country codes indicated in nodes
Internal
External
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Diagnosis - Absorbent Star
Absorbent star links external experts to internal
network
Absorbent Star

I4
Rest of Network
E1
I6
I3
E2
I1
I5
E3
I2
Internal
External
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Diagnosis - Diffusion
Internal cluster not connected to the rest of the
internal network
E1
E3
E2
I4
I3
Rest of Network
I5
I1
I8
I4
Isolated Internal Pocket
I7
I6


Internal
External
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Diagnosis - Robustness
Internal network not robust to loss of I3
E3
E5
E4
E1
I2
I1
E2
I3
I6
I5
I4


Internal
External
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Diagnosis - Vulnerability
Internal network vulnerable to external expert E1
I2
I3
I1
I7
I5
I6
I4
E1
I8
External Bridge
Internal
External
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Design

• Design small world external networks for
  exploration of disruptive technologies
• Design dense external networks for exploitation
  of existing technologies
• Design star external networks for mobilization
  of incremental, non-disruptive technologies

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Summary

• The Lovegety and SNIF underscore 21st century
  aspirations for more effective networking.
• Recent advances in cyberinfrastructure
  development provides the technological capability
  to more effectively leverage our networks.
• Recent advances in communication networks
  research provides important insights into the
  social and organizational motivations that
  explain how we leverage our networks.
• We are poised for the design, development, and
  deployment of large scale socio-technical network
  referral systems as part of the next generation
  cyberinfrastructure.

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Science of Networks in Communities nosh_at_uiuc.edu w
ww.uiuc.edu/ph/www/nosh