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Noshir Contractor

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Director, Age of Networks Initiative, Center for Advanced Study ... (KAME/NAME/D2K/Automap) 5. Statistical methods to. empirically validate networks ... – PowerPoint PPT presentation

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Title: Noshir Contractor


1
From Disasters to WoW Enabling Knowledge
Networks in 21st Century Organizational forms
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
Key Takeaways
  • Desire for social networking tools to assist
    multidisciplinary, interdisciplinary, and
    transdisciplinary (MIT) collaboration
  • Development of multi-theoretical multilevel
    (MTML) understandings of why we create, maintain,
    dissolve and reconstitute social network links.
  • Development of tools and algorithms to
    Discover, Diagnose, and Design (3D) more
    effective social networks
  • Opportunity to harvest empirical
    multi-dimensional network data from recent
    efforts on the Grid and Web 2.0.
  • Opportunity to enable more effective social
    networking within the Grid
  • Challenge to develop MTML 2.0 to explain creation
    of links in multidimensional networks

4
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.

5
Cognitive Knowledge Networks
Source Newsweek, December 2000
6
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
7
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 Tivo thinks I am gay and Amazon thinks I
am pregnant .
8
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
9
WHY DO WE CREATE, MAINTAIN, DISSOLVE, AND
RECONSTITUTE OUR COMMUNICATION AND KNOWLEDGE
NETWORKS?
10
Social DriversWhy do we create and sustain
networks?
  • 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 Contractor, N. S., Wasserman, S.
Faust, K. (2006). Testing multi-theoretical
multilevel hypotheses about organizational
networks An analytic framework and empirical
example. Academy of Management Review. Monge, P.
R. Contractor, N. S. (2003). Theories of
Communication Networks. New York Oxford
University Press.
11
Structural signatures of MTML
Theories of Self interest
Theories of Exchange
Theories of Balance
Theories of Collective Action
Theories of Homophily
Theories of Cognition
12
What Have We Learned About These Network
Mechanisms?
  • Research typically looks at only one of these
    mechanisms
  • The outcomes of these mechanisms often contradict
    one another
  • Some mechanisms are studied more often than
    others
  • Most research examines these mechanisms at one
    point in time

13
Enter ERGM Framework
  • Integrating exogenous and endogenous processes
    based on multiple theories at multiple levels
    leads to many possible realizations of the
    network.
  • The observed network is one realization of the
    many possible realizations of the network.
  • Confirmatory Network Analysis The question of
    interest in statistical modeling is whether the
    observed network exhibits the theoretically
    hypothesized structural tendencies.

14
A contextual meta-theory ofsocial drivers for
creating and sustaining communities
15
Projects Investigating Social Drivers for
Communities
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) CP2R
Collaboration for Preparedness, Response
Recovery (NSF) TSEEN Tobacco Surveillance
Evaluation Epidemiology Network (NSF, NIH,
CDC)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment) Second Life (Linden Labs, NCSA)
16
Contextualizing Goals of Communities
17
Projects Investigating Social Drivers for
Communities
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) CP2R
Collaboration for Preparedness, Response
Recovery (NSF) TSEEN Tobacco Surveillance
Evaluation Epidemiology Network (NSF, NIH,
CDC)
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment) Second Life (Linden Labs, NCSA)
18
3D Strategy for Enhancing CoP 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 CoPs internal
    and external 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.

19
PackEdge CoP Vital Statistics
  • Exploration
  • Scanning Access to expertise external to CoP
  • Absorption Ability to absorb expertise external
    to CoP
  • Vulnerability Brokered by members external to
    CoP
  • Exploitation
  • Diffusion Ability to diffuse expertise
    throughout CoP
  • Robustness Not relying on few critical CoP
    members to keep things together

20
Pre-wired PackEdge CoP Network
21
Re-wired PackEdge CoP Network
22
Wiring the PackEdge CoP Network for Success
  • Increase the likelihood to give and get
    information to the right target and source
    respectively
  • Benefits for CoP
  • Increase absorptive capacity from 45.3 to 53.4
  • Reduce number of steps for diffusion from 4.3 to
    2.6
  • Costs for CoP
  • Increase communication links of network leaders
    from 28 to 38 ( 150 new links).
  • Increase criticality of network leaders from 26.7
    to 48.5

23
FRAMEWORK FOR MTML MODELING OF NETWORK DYNAMICS
1. Extend theories to predict the dynamics of
networks (MTML)
Iterative refinements to theories about network
dynamics
Multi-level hypotheses and concepts to be measured
Generative mechanisms
Competence-based design of Cyberinfrastructure
4. Develop interventions including cyberinfrastru
cture tools to enable networks (CI-IKNOW)
3. Collect/capture longitudinal empirical network
data (KAME/NAME/D2K/Automap)
2. Develop agent-based computational models to
assess and evaluate alternative trajectories
of network dynamics (Repast/Blanche)
Web-based surveys, usage logs, text-mining, and
web-crawling tools to capture network dynamics
5. Statistical methods to empirically validate
networks dynamics predicted by agent based
models based on MTML theories (p /ERGM
techniques using MCMC methods)
Model predictions of networks
24
Projects Investigating Social Drivers for
Communities
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) CP2R
Collaboration for Preparedness, Response
Recovery (NSF) TSEEN Tobacco Surveillance
Evaluation Epidemiology Network (NSF, NIH,
CDC)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment) Second Life (Linden Labs, NCSA)
25
ICT Support in Emergency Management Networks

Drawing Analogies from Natural Systems
Project funded by NSF IT-Based Collaboration
Framework for Preparing Against, Responding to,
and Recovering (CP2R) from Disasters Involving
Critical Physical Infrastructures, (2004-2009,
2,370,000).  
26
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)
27
From insect colonies to emergency response
teamsClient-Server vs. P2P Network
28
Kelips P2P Architecture Introduction
  • Two components of Kelips (Gupta et al, 2003)
  • Layout of the P2P network
  • Maintain the balance between number of links
    needed for connecting the nodes in the network in
    an efficient manner and the cost of maintaining
    these links
  • Message Dissemination over the P2P network
    Gossip
  • Information of the membership whos currently
    active in the network whos in and whos out
  • Who knows what

Gupta, I., Birman, K., Linga, P., Demers, A.,
Renesse, R. v. (2003). Kelips Building an
efficient and stable P2P DHT through increased
memory and background overhead. Paper presented
at the the 2nd International workshop on
Peer-to-Peer Systems (IPTPS'03), Berkeley, CA.
29
Kelips Protocol to Generate P2P Network Layout
  • Randomly assign members to k different groups (k
    )
  • For instance, members of a network of size N
    100 are assigned to k 10 groups
  • Randomly create links between members, l outgoing
    links for each node (l O(log N))
  • For instance, in a network of size N 100, each
    node will have l O(2) 4-6 outgoing links

30
Limitations of KelipsMTML extensions
  • First responders are not likely to trust
    information from random others (Hollingshead
    Costa, 2005)
  • Network layout needs to take into account the
    social motivations for sharing information among
    first responders
  • The MTML (Multi-Theoretical MultiLevel) Model
    (Contractor et al, 2006 Monge Contractor, 2003
    ) provides a framework to investigate social
    motivations for why we create, maintain and
    dissolve communication networks

31
Network Layout Mechanism
  • Social Network Theories Applied
  • Theory of Social Exchange (SE)
  • Theory of Structural Hole (SH)
  • Strategies
  • Random Layout
  • SE/SH Network Layout
  • SESH Network Layout

32
Simulation Results
  • Repast (Recursive Porous Agent Simulation Toolkit
    )
  • Multi-Agent Computational Modeling Toolkit

33
Message Dissemination Comparing the Efficiency
of the Strategies
  • Using a random network, 20 out of 1000 nodes can
    not access the information. This is consistent
    with the high coverage predicted by the original
    Kelips model. However the coverage improves
    (fewer nodes without messages ) when Kelips is
    extended to include social exchange and
    structural holes as motivation to choose links.
  • The number of nodes not able to access the
    information decreases as the nodes are more
    likely to choose links on the basis of social
    exchange rather than structural holes
  • Further, the number of nodes not able to access
    the information is consistently lower when nodes
    choose links on the basis of social exchange and
    structural holes rather than social exchange or
    structural holes

34
Message Dissemination Comparing the Robustness
of the Strategies
  • The robustness improves (fewer nodes without
    messages ) when Kelips is extended to include
    social exchange and structural holes as
    motivation to choose links.
  • Further, the number of nodes not able to access
    the information is consistently lower when nodes
    choose links on the basis of social exchange and
    structural holes rather than social exchange or
    structural holes

35
Projects Investigating Social Drivers for
Communities
Business Applications PackEdge Community of
Practice (PG) Vodafone-Ericsson Club
for virtual supply chain management (Vodafone)
Science Applications CLEANER Collaborative
Large Engineering Analysis Network for
Environmental Research (NSF) Collaboration
for Preparedness, Response Recovery
(NSF) TSEEN Tobacco Surveillance Evaluation
Epidemiology Network (NSF, NIH, CDC)
Core Research Social Drivers for Creating
Sustaining Communities
Societal Justice Applications Cultural
Networks Assets In Immigrant Communities
(Rockefeller Program on Culture
Creativity) Economic Resilience NGO Community
(Rockefeller Program on Working Communities)
Entertainment Applications World of Warcraft
(NSF) Everquest (NSF, Sony Online
Entertainment) Second Life (Linden Labs, NCSA)
36
WoW Massively Multiplayer Online Role Playing
Game
37
Rise of WoW
Source http//www.mmogchart.com/
38
Goals of WoW Community
  • Teams perform diverse quests within the game
    environment, typically varying in length from one
    hour to several days, with the goal of achieving
    an objective, gaining resources, and increasing
    experience.
  • Exploiting, Bonding Swarming

39
Contextualizing Goals of WoW
40
Mapping Goals to Theories WoW Gaming Community
41
Data Collection
  • Data were collected from all 184 individuals who
    belonged to 16 guilds at 3 points in time.
  • T1 initial contact and survey administration
  • T2 two weeks after initial survey administration
  • T3 four weeks after initial survey
    administration
  • Demographic information
  • Gender
  • Female members (21.7)
  • Male members (78.3)
  • Ethnicity
  • Caucasian (79.3)
  • Asian/ Pacific Islander (15.2)
  • African American (2.2)
  • Hispanic/Latino (1.1)
  • Native American (1.1)

42
Information Retrieval - Time One
43
Information Retrieval - Time Two
44
Information Retrieval -Time Three
45
Density of Communication Ties Decreases over Time
46
Unraveling the Structural Signatures
  • Incentive for creating a WoW link with someone
  • -1.08 (cost of creating a link)
    Self-interest
  • 0.29 (benefit of reciprocating) Exchange
  • 3.07 (benefit for being a friend of a friend)
  • Balance
  • 0.04 (benefit of connecting to an expert)
  • Cognition

All coefficients significant at 0.05 level
47
Summary
  • Theories about the social motivations for
    creating, maintaining, dissolving and re-creating
    social network ties in multidimensional networks
  • Development of cyberinfrastructure/Web 2.0
    provide the technological capability to capture
    relational metadata needed to more effectively
    understand (and enable) communities.
  • Computational modeling techniques to model
    multidimensional network dynamics.
  • Exponential random graph modeling techniques to
    empirically validate the local structural
    signatures that explain emergent global network
    properties

48
SONIC Research Team Members
Andy Don Research Programmer NCSA, UIUC
Steven Harper Postdoc NCSA, UIUC
Hank Green Research Scientist NCSA, UIUC
Alex Yahja Postdoc NCSA, UIUC
Nat Bulkley Postdoc NCSA, UIUC
Chunke Su Graduate Research Assistant Speech
Communication, UIUC
Mengxiao Zhu Graduate Research Assistant Speech
Communication, UIUC
York Yao Research Programmer NCSA, UIUC
Diana Jimeno-Ingrum Graduate Research
Assistant Labor Industrial Relations, UIUC
Annie Wang Graduate Research Assistant Speech
Communication, UIUC
49
Acknowledgements
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