Noshir Contractor

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Coevolution of knowledge networks and 21st
century cyberinfrastructure
Noshir Contractor Professor, Departments of
Speech Communication Psychology Co-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
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1. Turn on power set MODE with MODE button. You
   can confirm the MODE you chose as the red
   indicator blinks.
2. Lamp blinks when (someone with) a Lovegety for
   the opposite sex set under the same MODE as yours
   comes near.
3. 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.

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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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Amazon Purchase Network of Books on Network
Theory
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Amazon buyers Network of Top Selling Books on
Network Science
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Amazon buyers Network of Top Selling Books on
Network Society
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TECLab/SONIC Projects on Enabling Networks

• Networks to enable Cyberinfrastructure, NCSA/NSF
• Emergency Response Networks, NSF-ITR
• Tobacco Surveillance, Research Evaluation
  Networks, NCI/NIH
• Transnational Immigrant Networks, Rockefeller
  Foundation
• Economic Justice Networks, Rockefeller Foundation
• Communities of Practice Networks, Procter Gamble
• Food Safety Networks, UIUC Cross-Campus
  Initiative John Deere
• Global Supply Chain Infrastructure, Vodafone

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Science and Engineering Cyberinfrastructures
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Geosciences Cyberinfrastructures
SEEK The Science Environment for Ecological
Knowledge
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Testbed Communities Partners

• Collaborative for Large-scale Engineering
  Analysis Network for Environmental Research
  (CLEANER) Barbara Minsker, UIUC
• Tobacco Systems Integration Grid (Tobacco SIG)
  Scott Leischow, NCI
• Social Network Analysis CI (SNAC) Katy Borner,
  Indiana U
• Engaging People in Communities (EPIC) Scott
  Lathrop, NCSA Education Outreach

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TECLab/SONIC Projects on Enabling Networks

• Networks to enable Cyberinfrastructure, NCSA/NSF
• Emergency Response Networks, NSF-ITR
• Tobacco Surveillance, Research Evaluation
  Networks, NCI/NIH
• Transnational Immigrant Networks, Rockefeller
  Foundation
• Economic Justice Networks, Rockefeller Foundation
• Communities of Practice Networks, Procter Gamble
• Food Safety Networks, UIUC Cross-Campus
  Initiative John Deere
• Global Supply Chain Infrastructure, Vodafone

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ICT Support in Emergency Management Networks

Drawing Analogies from Natural Systems
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Natural System 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 System 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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TECLab/SONIC Projects on Enabling Networks

• Networks to enable Cyberinfrastructure, NCSA/NSF
• Emergency Response Networks, NSF-ITR
• Tobacco Surveillance, Research Evaluation
  Networks, NCI/NIH
• Transnational Immigrant Networks, Rockefeller
  Foundation
• Economic Justice Networks, Rockefeller Foundation
• Communities of Practice Networks, Procter Gamble
• Food Safety Networks, UIUC Cross-Campus
  Initiative John Deere
• Global Supply Chain Infrastructure, Vodafone

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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
Source Contractor, 2001
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Human A Human B Human C Non Human Agent X Non Human Agent Y
Human A
Human B
Human C
Non Human Agent X
Non Human Agent Y
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?
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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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ANALYZING ENABLING NETWORKS IN
CYBERINFRASTRUCTURE
1. Extend theories to predict the dynamics of
a cybercommunity (MTMLEntomology Epidemiology
?)
Iterative Refinements to theories about dynamics
of cybercommunity
Multi-level hypotheses and concepts to be measured
Generative mechanisms
Competence-based design of Cyberinfrastructure
4. Develop and introduce cyberinfrastructure
networking tools to enable the
cybercommunity (Adhoc/Sensor networks, IKNOW)
3. Collect longitudinal empirical data from
participants in cybercommunity (KAME/NAME)
2. Develop agent-based computational models to
assess and evaluate alternative scenarios for
the long term dynamics of the cybercommunity (Bla
nche)
Web-based surveys and real time observation and
computer-captured data from cybercommunity
activities
5. Statistical methods to empirically validate
the dynamics of the cybercommunity as predicted
by theories and models (p/ERGM and MCMC
techniques)
Model predictions of cybercommunity
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Co-evolution of knowledge networks and 21st
century organizational forms

• NSF KDI Initiative 1999-04. 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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Integrating exogenous and endogenous processes
based on multiple theories at multiple levels
leads to many possible realizations of the
network.
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p Framework

• The observed network is one realization of the
  many possible random realizations of the network.
• Confirmatory Network Analysis The questions of
  interest in statistical modeling is whether the
  observed network exhibits the theoretically
  hypothesized structural tendencies.
• The statistical estimates of p parameters
  indicate whether network realizations with the
  theoretically hypothesized properties have
  significantly large probabilities of being
  observed in the network data collected.

Source 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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Modeling p Random Graph Distributions

• For an observed network, which we consider to be
  a realization x of a random array X, we assume
  the existence of a dependence graph D for the
  random array X.
• The edges of D are crucial here consider the set
  of edges, and determine if there are any complete
  subgraphs, or cliques found in the dependence
  graph.
• For a general dependence graph, a subset A of the
  set of relational ties ND is complete if every
  pair of nodes in A (that is, every pair of
  relational ties) is linked by an edge of D. A
  subset comprising a single node is also regarded
  as complete.
• These cliques specify which subsets of relational
  ties are all pair wise, conditionally dependent
  on each other.

Source Carrington, P., Scott, J., Wasserman,
S. (Eds.). (2005). Models and Methods in Social
Network Analysis. New York Cambridge
University Press.
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Using Dependence Graphs to Model p Random Graph
Distributions

• The Hammersley-Clifford theorem (Besag, 1974)
  provides the important link between the
  dependence graph and the structure of the model
  that encapsulates its dependence assumptions.
• The theorem establishes that the probability
  model for the random multigraph, X, depends on
  the complete subgraphs of the dependence graph,
  D.
• A complete subgraph, or clique, is a subset of
  nodes in the dependence graph every pair of which
  is linked by an edge. A subset consisting of a
  single node is also regarded as complete.
• Each complete subgraph corresponds to a
  configuration of possible ties in the network.
• There is a model parameter corresponding to each
  complete subgraph in the dependence structure
  (and so to each corresponding configuration of
  possible ties).
• The parameter for a particular configuration
  reflects the effect of observing that
  configuration on the likelihood of the network.

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Using Dependence Graphs to Model p Random Graph
Distributions

• The random graph model is of the following
  exponential form
• Pr(X x) p(x) ?-1 exp??A?ND?AzA(x)
• where
• x is a realization of the random graph, X
• ? ?x exp??A?ND?AzA(x) is a normalizing
  quantity the summation is over all
• subsets A of nodes of D
• z A(x) is the empirically observed network
  statistic in x corresponding to the subgraph A of
  D and is given by z A(x) ? Xij?A xij
• ?A is the parameter corresponding to the subgraph
  A of D and ?A 0 whenever the subgraph induced
  by the nodes in A is not a complete subgraph of D.

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Interpreting Parameters in the Model p Random
Graph Distributions

• The random graph model is of the following
  exponential form
• Pr(X x) p(x) ?-1 exp??A?ND?AzA(x)
• The quantities zA(x) are calculated from the
  observed network and correspond to the
  hypothesized structural tendencies expressed in
  the dependence graph.
• ?A are parameters corresponding to the cliques A
  of D. These parameters express the importance of
  the associated structural tendency for the
  probability of the graph.

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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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3D Vision for SONIC

• Discovery tools to effectively and efficiently
  foster network links from people to other people,
  knowledge, and artifacts (data sets/streams,
  analytic tools, visualization tools, documents,
  etc.) within the cybercommunities.
• Diagnostic tools to assess the health of
  knowledge networks within cybercommunities
  scanning, absorptive capacity, diffusion,
  robustness, vulnerability.
• Design or re-wire networks using social and
  organizational incentives as well as
  computationally advanced and intensive network
  referral systems to enhance evolving and mature
  communities.

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IKNOW Demo
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Summary

• 21st century cyberinfrastructure, like the
  Lovegety, necessitates studying the emergence
  creation, maintenance, dissolution, and
  reconstitution of networks.
• Research on emergence of networks requires an
  analytic approach that empirically tests the
  simultaneous influence of multi-theoretical
  explanations at multiple levels.
• p /ERGM confirmatory network analytic methods
  have proven useful in simultaneously testing
  hypotheses using this framework.

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• Contact information
• nosh_at_uiuc.edu
• www.uiuc.edu/ph/www/nosh