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Noshir ContractorProfessor, Departments of Speech Communication & PsychologyProfessor, Departments of Speech Communication & Psychology
Co-Director, Age of Networks, Initiative, Center for Advanced StudyCo-Director, Age of Networks, Initiative, Center for Advanced StudyDirector, Science of Networks in Communities -Director, Science of Networks in Communities -
National Center for Supercomputing ApplicationsNational Center for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignUniversity of Illinois at Urbana-Champaign
[email protected]@uiuc.edu
Coevolution of knowledge networks and 21st century cyberinfrastructure
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.
Aphorisms about Networks
Social NetworksSocial 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.
Source: Newsweek, December 2000
Cognitive Knowledge Networks
Amazon Purchase Network of Books on “Network Theory”
Amazon buyers Network of Top Selling Books on “Network Science”
Amazon buyers Network of Top Selling Books on “Network Society”
TECLab/SONIC Projects on Enabling Networks
Networks to enable CyberinfrastructureNetworks to enable Cyberinfrastructure, , NCSA/NSF NCSA/NSF
Emergency Response NetworksEmergency Response Networks, , NSF-ITRNSF-ITR
Tobacco Surveillance, Research & Evaluation Networks, Tobacco Surveillance, Research & Evaluation Networks, NCI/NIHNCI/NIH
Transnational Immigrant Networks,Transnational Immigrant Networks, Rockefeller Foundation Rockefeller Foundation
Economic Justice Networks, Economic Justice Networks, Rockefeller FoundationRockefeller Foundation
Communities of Practice Networks, Communities of Practice Networks, Procter &GambleProcter &Gamble
Food Safety Networks, UIUC Cross-Campus Initiative & Food Safety Networks, UIUC Cross-Campus Initiative & John DeereJohn Deere
Global Supply Chain Infrastructure, Global Supply Chain Infrastructure, VodafoneVodafone
Science and Engineering Cyberinfrastructures
Geosciences Cyberinfrastructures
SEEK: The Science Environment for Ecological Knowledge
Testbed Communities: Partners
Collaborative for Large-scale Engineering Collaborative for Large-scale Engineering Analysis Network for Environmental Research Analysis Network for Environmental Research (CLEANER): (CLEANER): Barbara Minsker, UIUCBarbara Minsker, UIUC
Tobacco Systems Integration Grid (Tobacco Tobacco Systems Integration Grid (Tobacco SIG): SIG): Scott Leischow, NCIScott Leischow, NCI
Social Network Analysis CI (SNAC): Social Network Analysis CI (SNAC): Katy Katy Borner, Indiana UBorner, Indiana U
Engaging People in Communities (EPIC): Engaging People in Communities (EPIC): Scott Lathrop, NCSA Education & OutreachScott Lathrop, NCSA Education & Outreach
TECLab/SONIC Projects on Enabling Networks
Networks to enable CyberinfrastructureNetworks to enable Cyberinfrastructure, , NCSA/NSF NCSA/NSF
Emergency Response NetworksEmergency Response Networks, , NSF-ITRNSF-ITR
Tobacco Surveillance, Research & Evaluation Networks, Tobacco Surveillance, Research & Evaluation Networks, NCI/NIHNCI/NIH
Transnational Immigrant Networks,Transnational Immigrant Networks, Rockefeller Foundation Rockefeller Foundation
Economic Justice Networks, Economic Justice Networks, Rockefeller FoundationRockefeller Foundation
Communities of Practice Networks, Communities of Practice Networks, Procter &GambleProcter &Gamble
Food Safety Networks, UIUC Cross-Campus Initiative & Food Safety Networks, UIUC Cross-Campus Initiative & John DeereJohn Deere
Global Supply Chain Infrastructure, Global Supply Chain Infrastructure, VodafoneVodafone
ICT Support in Emergency Management Networks
{
Drawing Analogies from Natural Systems
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 Bees’ setting and objectives in foraging [Seeley, et al. 1991][Seeley, et al. 1991] resembles disaster resembles disaster relief response scenario relief response scenario (collective decision-making).(collective decision-making).
Problem: Information Overload
Hundreds or Thousands of first Hundreds or Thousands of first responders operate sharing responders operate sharing couple of voice channels (radio, couple of voice channels (radio, cell-phones) cell-phones) [Domel, 2001][Domel, 2001]
http://www.hollandsentinel.com/images/031503/Borculofire4.jpg
If technology provides a mean to enhance delivery and media of If technology provides a mean to enhance delivery and media of information, we envision this problem would increaseinformation, we envision this problem would increase
Information Overload: Ants
HOW
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?).
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.
Foraging Model [Seeley, 1991]
Honey Bees (Apis melifera)
At hive unloadingnectar from A
(HA)
At hive unloadingnectar from B
(HB)
Foraging at nectarsource A
(A)
Foraging at nectarsource B
(B)
Followingother dances
(F)
Dancing for A(DA)
Dancing for B(DB)
p3 p1 p7p5
p4
p6p2
fx A fx B
1-fx B1-fx A
fd B(1-fx B)fd A(1-fx A)
(1-fd B)(1-fx B)(1-fd A)(1-fx A)
ff A ff B
The system evaluates ALL the information, though individuals evaluate only partial information
Natural System: Honey Bees
TECLab/SONIC Projects on Enabling Networks
Networks to enable CyberinfrastructureNetworks to enable Cyberinfrastructure, , NCSA/NSF NCSA/NSF
Emergency Response NetworksEmergency Response Networks, , NSF-ITRNSF-ITR
Tobacco Surveillance, Research & Evaluation Networks, Tobacco Surveillance, Research & Evaluation Networks, NCI/NIHNCI/NIH
Transnational Immigrant Networks,Transnational Immigrant Networks, Rockefeller Foundation Rockefeller Foundation
Economic Justice Networks, Economic Justice Networks, Rockefeller FoundationRockefeller Foundation
Communities of Practice Networks, Communities of Practice Networks, Procter &GambleProcter &Gamble
Food Safety Networks, UIUC Cross-Campus Initiative & Food Safety Networks, UIUC Cross-Campus Initiative & John DeereJohn Deere
Global Supply Chain Infrastructure, Global Supply Chain Infrastructure, VodafoneVodafone
Human Agent to Human Agent Communication
Retrieving from knowledge repository
Publishing to knowledge repository
Non Human Agent to Non Human Agent Communication
Non Human Agent (webbots, avatars, databases,
“push” technologies) To Human Agent
INTERACTION NETWORKS
Source: Contractor, 2001
Human Agent’s Perception of What Another Human Agent
Knows
Non Human Agent’s Perception of Resources in a Non Human Agent
Non Human Agent’s Perception of what a Human
Agent knows
Human Agent’s Perception of Provision of Resources in a
Non Human Agent
COGNITIVE KNOWLEDGE NETWORKS
Source: Contractor, 2001
Human AHuman A Human BHuman B Human CHuman C Non Non Human Human Agent XAgent X
Non Non Human Human Agent YAgent Y
Human AHuman A
Human BHuman B
Human CHuman C
Non Human Non Human Agent XAgent X
Non Human Non Human Agent YAgent 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
WHY DO WE CREATE,
MAINTAIN, DISSOLVE, AND
RECONSTITUTE OUR COMMUNICATION AND
KNOWLEDGE NETWORKS?
Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Oxford
University Press.
Why do actors create, maintain, dissolve, and reconstitute
network links? Theories of self-interestTheories of self-interest Theories of social and Theories of social and
resource exchangeresource exchange Theories of mutual Theories of mutual
interest and collective interest and collective actionaction
Theories of contagionTheories of contagion Theories of balanceTheories of balance Theories of homophilyTheories of homophily Theories of proximityTheories of proximity Theories of co-evolutionTheories 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 Contractor, N. S., Wasserman, S. & Faust, K. (in press). Testing multi-theoretical
multilevel hypotheses about organizational networks: An analytic framework and multilevel hypotheses about organizational networks: An analytic framework and empirical example. Academy of Management Reviewempirical example. Academy of Management Review . .
1. Extend theories to predictthe dynamics of acybercommunity
(MTML+Entomology+Epidemiology + ?)
2. Develop agent-based computational models to assess
and evaluate alternative scenarios for the long term dynamics of
the cybercommunity(Blanche)
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)
5. Statistical methods to empirically validate the
dynamics of thecybercommunity as predicted by
theories and models(p*/ERGM and MCMC techniques)
Generative mechanisms
Model predictions of cybercommunity
Multi-level hypotheses andconcepts to be measuredCompetence-based design of Cyberinfrastructure
Web-based surveys and real timeobservation and computer-captured data from
cybercommunity activities
Iterative Refinements to theories about dynamics of cybercommunity
ANALYZING & ENABLING NETWORKS IN CYBERINFRASTRUCTURE
Co-evolution of knowledge networks and 21st century organizational forms
NSF KDI Initiative 1999-04. PI: Noshir NSF KDI Initiative 1999-04. PI: Noshir Contractor, University of Illinois.Contractor, University of Illinois.
Co-P.I.s: Monge, Fulk, Bar (USC), Levitt, Co-P.I.s: Monge, Fulk, Bar (USC), Levitt, Kunz (Stanford), Carley (CMU), Wasserman Kunz (Stanford), Carley (CMU), Wasserman (Indiana), Hollingshead (Illinois).(Indiana), Hollingshead (Illinois).
Three dozen industry partners (global, profit, Three dozen industry partners (global, profit, non-profit): non-profit): Boeing, 3M, NASA, Fiat, U.S. Army, American Boeing, 3M, NASA, Fiat, U.S. Army, American
Bar Association, European Union Project Team, Bar Association, European Union Project Team, Pew Internet Project, etc.Pew Internet Project, etc.
Transactive MemoryTransactive Memory Perception of Other’s Perception of Other’s
KnowledgeKnowledge Communication to Communication to
Allocate Information Allocate Information
Social Exchange - Retrieval by coworkers on other topics
Public Goods / Transactive Memory
–Allocation to the Intranet–Retrieval from the Intranet–Perceived Quality and Quantity of Contribution to the Intranet
Inertia Components–Collaboration–Co-authorship
–Communication
Communication to Retrieve Information
Proximity-Work in the same location
Integrating exogenous and endogenous processes based on multiple theories at multiple levels leads to many possible realizations of the network.
p* Framework
The observed network is one realization of the many The observed network is one realization of the many possible random realizations of the network.possible random realizations of the network.
Confirmatory Network Analysis: The questions of Confirmatory Network Analysis: The questions of interest in statistical modeling is whether the interest in statistical modeling is whether the observed network exhibits the theoretically observed network exhibits the theoretically hypothesized structural tendencies.hypothesized structural tendencies.
The statistical estimates of p* parameters indicate The statistical estimates of p* parameters indicate whether network realizations with the theoretically whether network realizations with the theoretically hypothesized properties have significantly large hypothesized properties have significantly large probabilities of being observed in the network data probabilities of being observed in the network data collected. collected.
Source: Contractor, N. S., Wasserman, S. & Faust, K. (in press). Testing Source: Contractor, N. S., Wasserman, S. & Faust, K. (in press). Testing multi-theoretical multilevel hypotheses about organizational networks: multi-theoretical multilevel hypotheses about organizational networks: An analytic framework and empirical example. An analytic framework and empirical example. Academy of Management Academy of Management
Review.Review.
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).Source: Carrington, P., Scott, J., & Wasserman, S. (Eds.). (2005). Models and Methods in Social Network Analysis. Models and Methods in Social Network Analysis. New York:New York: Cambridge Cambridge University PressUniversity Press..
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.
Using Dependence Graphs to Model p* Random Graph Distributions
The random graph model is of the following exponential form:
Pr(X = x) = p*(x) = κ-1 exp∑ANDλAzA(x)}
where:
x is a realization of the random graph, X;
κ = ∑x exp∑ANDλAzA(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) = Π Xij∈A xij;
λA is the parameter corresponding to the subgraph A of D; and λA = 0 whenever the subgraph induced by the nodes in A is not a complete subgraph of D.
Interpreting Parameters in the Model p* Random Graph Distributions
The random graph model is of the following exponential form:
Pr(X = x) = p*(x) = κ-1 exp∑ANDλAzA(x)}
The quantities zA(x) are calculated from the observed network and correspond to the hypothesized structural tendencies expressed in the dependence graph.
λA 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.
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
Motivation for Information Retrieval in Knowledge Networks
3D Vision for SONIC DDiscovery toolsiscovery tools to effectively and efficiently foster to effectively and efficiently foster
network links from people to other people, network links from people to other people, knowledge, and artifacts (data sets/streams, analytic knowledge, and artifacts (data sets/streams, analytic tools, visualization tools, documents, etc.) within the tools, visualization tools, documents, etc.) within the cybercommunities.cybercommunities.
DDiagnostic tools to assess the “health” of knowledge iagnostic tools to assess the “health” of knowledge networks within cybercommunities: scanning, networks within cybercommunities: scanning, absorptive capacity, diffusion, robustness, absorptive capacity, diffusion, robustness, vulnerability.vulnerability.
DDesign or re-wire networks using social and esign or re-wire networks using social and organizational incentives as well as computationally organizational incentives as well as computationally advanced and intensive network referral systems to advanced and intensive network referral systems to enhance evolving and mature communities.enhance evolving and mature communities.
IKNOW Demo
Summary
2121stst century cyberinfrastructure, like the century cyberinfrastructure, like the LovegetyLovegety, , necessitates studying the emergence – creation, necessitates studying the emergence – creation, maintenance, dissolution, and reconstitution – of maintenance, dissolution, and reconstitution – of networks.networks.
Research on emergence of networks requires an Research on emergence of networks requires an analytic approach that empirically tests the analytic approach that empirically tests the simultaneoussimultaneous influence of multi-theoretical influence of multi-theoretical explanations at multiple levels. explanations at multiple levels.
p* /ERGM confirmatory network analytic methods p* /ERGM confirmatory network analytic methods have proven useful in simultaneously testing have proven useful in simultaneously testing hypotheses using this framework. hypotheses using this framework.
