noshir contractor jane s. & william j. white professor of behavioral sciences jane s. &...

Noshir ContractorJane S. & William J. White Professor of Behavioral SciencesJane S. & William J. White Professor of Behavioral Sciences

Professor of Ind. Engg & Mgmt Sciences, McCormick School of Engineering Professor of Communication Studies, School of Communication &

Professor of Management & Organizations, Kellogg School of Management,Director, Science of Networks in Communities (SONIC) Research Laboratory

[email protected]

Supported by NSF : OCI-0753047, IIS-0729505, IIS-0535214, SBE-0555115

From Disasters to WoW: Enabling Knowledge Networks in the 21st century

SONIC

Advancing the Science of Networks in Communities

Each circle (node) represents one person in the data set. There are 2200

persons in this subcomponent of the social network. Circles with red borders

denote women, and circles with blue borders denote men. The size of each

circle is proportional to the person's body-mass index. The interior color of the

circles indicates the person's obesity status: yellow denotes an obese

person(body-mass index, 30) and green denotes a non-obese person. The

colors of the ties between the nodes indicate the relationship between them:

purple denotes a friendship or marital tie and orange denotes a familial tie.

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.

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Cognitive Knowledge Networks

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Multidimensional Networks in Web 2.0Multiple Types of Nodes and Multiple Types of Relationships

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WHY DO WE CREATE,

MAINTAIN, DISSOLVE, AND

RECONSTITUTE OUR COMMUNICATION AND

KNOWLEDGE NETWORKS?

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Social Drivers:Why do we create and sustain

networks? Theories of self-Theories of self-

interestinterest 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-Theories of co-

evolutionevolutionSources: 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.

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Exploring Exploiting Mobilizing Bonding Swarming

Theories of Self-Interest + -- Theories of Collective Action + + +

Theories of Cognition + + + Theories of Balance -- + +

Theories of Exchange + + Theories of Contagion + + Theories of Homophily -- + Theories of Proximity -- + +

A contextual “meta-theory” ofsocial drivers for creating and sustaining

communities

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Core Research

Social Drivers for Creating & Sustaining

Communities

Business Applications

PackEdge Community of Practice (P&G)

Societal Justice Applications

Cultural & Networks AssetsIn Immigrant Communities (Rockefeller Program on Culture & Creativity)

Mapping Digital Media and Learning Networks(MacArthur Foundation)

Entertainment Applications

Virtual Worlds Exploratorium (NSF, Sony Online Entertainment, Linden Labs)

Science ApplicationsCI-Scope: Understanding & Enabling CI in Virtual Communities (NSF)

CP2R: Collaboration for Preparedness,Response & Recovery (NSF)

TSEEN: Tobacco Surveillance Evaluation & Epidemiology Network (NSF, NIH, CDC)

Projects Investigating Social Drivers for Communities

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Exploring Exploiting Mobilizing Bonding Swarming

Emergency Response Community

+ + +

WoW Gaming Community + + + Mexican Immigrant

Community + +

PackEdge Communities of Practice

+ + +

Economic Resilience NGO Community

+ +

Tobacco Surveillance, Evaluation & Epidemiology

Community + +

Environmental Engineering Community

+ + +

Contextualizing Goals of Communities

Challenges of empirically testing, extending, and exploring theories about networks … until now

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Enter Semantic Web/Web 2.0Its all about “Relational Metadata”

Technologies that “Technologies that “capturecapture” communities’ relational meta-data ” communities’ relational meta-data (Pingback and trackback in interblog networks, blogrolls, data (Pingback and trackback in interblog networks, blogrolls, data provenance)provenance)

Technologies to “Technologies to “tagtag” communities’ relational metadata (from Dublin ” communities’ relational metadata (from Dublin Core taxonomies to folksonomies (‘wisdom of crowds’) like Core taxonomies to folksonomies (‘wisdom of crowds’) like Tagging pictures (Flickr)Tagging pictures (Flickr) Social bookmarking (del.icio.us, LookupThis, BlinkList)Social bookmarking (del.icio.us, LookupThis, BlinkList) Social citations (CiteULike.org)Social citations (CiteULike.org) Social libraries (discogs.com, LibraryThing.com)Social libraries (discogs.com, LibraryThing.com) Social shopping (SwagRoll, Kaboodle, thethingsiwant.com)Social shopping (SwagRoll, Kaboodle, thethingsiwant.com) Social networks (FOAF, XFN, MySpace, Facebook)Social networks (FOAF, XFN, MySpace, Facebook)

Technologies to “Technologies to “manifestmanifest” communities’ relational metadata ” communities’ relational metadata (Tagclouds, Recommender systems, Rating/Reputation systems, ISI’s (Tagclouds, Recommender systems, Rating/Reputation systems, ISI’s HistCite, Network Visualization systems)HistCite, Network Visualization systems)

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Text Mining Web crawlingWeb of Science Citation

CATPAC

UBERLINK

Digital Harvesting of Relational Metadata

CI-KNOW Analyses and Visualizations SONIC


CI-KNOW: Harvesting the online community’s relational meta-data

INPUTS

Cybercommunity Resources

Cyberinfrastructure Use

External Resources

Generating a Multi-

Dimensional network

Network Analysis

PROCESSES

Network Maps

Network Referrals

OUTPUTS

Network Diagnostics

Users’ Profiles

Documents

Collaboration Tools

Datasets

Analysis Tools

Bibliographic DBsPersonal WebsitesOrganizational

WebsitesProject WebsitesPatent Databases

Linking all data together

1. Algorithms to generate Network Referrals

2. Algorithms to create Network Maps

3. Algorithms to compute Network Diagnostics

User activity logs related to cyberinfrastructure

Downloading Presentations

Using Tools to Analyze Datasets

Using Chats, Forum

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INPUTS

Cybercommunity Resources

Cyberinfrastructure Use

External Resources

Generating a Multi-

Dimensional network

Network Analysis

PROCESSES

Network Maps

Network Referrals

OUTPUTS

Network Diagnostics

1. Who to contact for what topic

2. What tools to use for what data

3. What dataset to analyze for what concepts

4. What papers to read for what keywords

1. What nodes are important for what relations

2. The amount of scanning, absorption, diffusion, robustness, vulnerability in a network

CI-KNOW: Harvesting the online community’s relational meta-data

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Core Research


Communities












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Hurricane Katrina 2005Formed:Formed: Aug 23, 2005Aug 23, 2005Dissipated:Dissipated: Aug 31, 2005Aug 31, 2005Highest wind:Highest wind: 175 mph175 mphLowest press:Lowest press: 902 mbar902 mbarDamages:Damages: $81.2 Billion$81.2 BillionFatalities:Fatalities: >1,836>1,836Areas affected:Areas affected: Bahamas,Bahamas, South Florida, South Florida,

Cuba, Louisiana Cuba, Louisiana (especially (especially Greater New Greater New Orleans), Orleans), Mississippi, Mississippi, Alabama, Alabama, Florida Florida Panhandle, most Panhandle, most of eastern North of eastern North AmericaAmerica

Map source: http://hurricane.csc.noaa.gov/

8/23

8/24

8/25

8/268/27

8/28

8/29

8/30

8/31

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SITREP Content

Basic Format / InformationBasic Format / Information1.1. Situation (What, Where, and When)Situation (What, Where, and When)

2.2. Action in ProgressAction in Progress

3.3. Action PlannedAction Planned

4.4. Probable Support Requirements and/or Probable Support Requirements and/or Support AvailableSupport Available

5.5. Other itemsOther items

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Typical SITREP *Colorado Division of Emergency Management SITUATION REPORT 2005-6 (Hurricane Katrina) August 30, 2005*

*Event Type:* Hurricane Response

*Situation:* On August 29, Hurricane Katrina hit the gulf coast east of NewOrleans. It was considered a Category 5 Hurricane, which brings winds ofover 155mph and storm surge of 18 feet above normal. Massive property damagehas occurred and undetermined number of deaths and injuries.

Colorado response to date include two deployments:- Two members from the Division of Emergency Management to the Louisiana EOC, departed on August 29. · · ·

*Weather Report:* Katrina is moving toward the north-northeast near 18 mph.A turn toward the northeast and a faster forward speed is expected duringthe next 24 hours. This motion should bring the cent · · ·

*Agencies Involved:* Colorado Department of Military and Veteran Affairs,Department of Local Affairs, Division of Emergency Management, Governor'sOffice.* *

*Additional Assistance Requested:* Type III teams, consisting of Operations,Plans, and Logistics personnel (two individuals for each area). These teamscould deploy to Alabama, Louisiana, and/or Mississippi. Teams will beat either working the State or Parish/County EOCs. · · ·

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Human Coding Procedure

Using an HTML editor to mark entities Using an HTML editor to mark entities (people, organizations, locations, concepts)(people, organizations, locations, concepts)

as bold and include a unique HTML tag as bold and include a unique HTML tag

<b><a name=“F10005505a00003”></a>FEMA</b><b><a name=“F10005505a00003”></a>FEMA</b>

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Automatic Coding D2K – The Data to Knowledge application D2K – The Data to Knowledge application

environment is a rapid, flexible data mining environment is a rapid, flexible data mining and machine learning systemand machine learning system

Automated processing is done through Automated processing is done through creating itineraries that combine processing creating itineraries that combine processing modules into a workflowmodules into a workflow

Developed by the Developed by the

Automated LearningAutomated Learning

Group at NCSAGroup at NCSA SONIC


Time Slice 1: 8/23 to 8/25/2005

ARCSAL

FEMA

Shelter

TX

KY

AL

LA

NO

Gov Bush

FL

Petroleum Network formed Early

Florida is the Topicof the Conversation

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Time Slice 1 to 2

ARCSAL

FEMA

Shelter

TX

KY

AL

LA

NO

Gov Bush

FL

Power

FP&L

GA

Military

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Time Slice 2: 8/26 to 8/27/2005

ARC

SAL

FEMA

Shelter

TXMSLA

NO

Gov Bush

FL Power

FP&L

GA

Military

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Time Slice 2 to 3

ARC

SAL

FEMA

Shelter

TXMSLA

NO

Gov Bush

FL

Power

FP&L

GA

Military

NC

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Time Slice 3: 8/28 to 8/29/2005

ARC

FEMA

Shelter

TX

MS LA

NO

Gov Bush

FL

PowerFP&L

NC

Military

GA

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Time Slice 3 to 4

ARC

FEMA

Shelter

TX

MS LA

NO

Gov Bush

FL

PowerFP&L

NC

Military

GA

AL Power

S & R

National Guard

AL

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Time Slice 4: 8/30 to 8/31/2005

ARC

FEMA

Shelter

TX

MS

LA

NO

FLPower

FP&LNC

GA

AL Power

S & R

National Guard

AL

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Time Slice 4 to 5

ARC

FEMA

Shelter

TX

LA

NO

FLPower

FP&LNC

GA

AL Power

S & R

National Guard

MS

AL

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Time Slice 5: 9/1 to 9/2/2005

ARC

FEMA

Shelter

TX

MS LA

NO

FL

Power

NC

GA

AL Power

S & R

National Guard

AL

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Time Slice 5 to 6

ARC

FEMA

Shelter

TX

MS LA

NO

FL

Power

GA

AL Power

S & R

National Guard

AL

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Time Slice 6: 9/3 to 9/4/2005

ARC

FEMA

Shelter

TX

MS

LA

NO

FL

OutagesGA

AL Power

Urban S & R

National Guard

AL

S & R

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Betweeness Centrality

0

50

100

150

200

250

1 2 3 4 5 6 7

Time Slice

Ran

k

American Red Cross FEMA

Change in Network Centrality Rankings

• “American Red Cross” starts in the 200s and moves to the teens• “FEMA” starts in the 20s, moves to the teens, and ends in the 60s

FEMA drops rank and American Red Cross moves up

Crossover where American Red Cross becomes relatively more central than FEMA (Sep 1, 2005)

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Core Research


Communities












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3D Strategy for Enhancing Knowledge Networks

DDiscoveryiscovery: Effectively and efficiently foster network links : Effectively and efficiently foster network links from people to other people, knowledge, and artifacts from people to other people, knowledge, and artifacts (data sets/streams, analytic tools, visualization tools, (data sets/streams, analytic tools, visualization tools, documents, etc.) documents, etc.) ““If only If only NSFNSF knows what knows what NSF NSF knows”.knows”.

DDiagnosisiagnosis: Assess the “health” of internal and external : Assess the “health” of internal and external networks - in terms of scanning, absorptive capacity, networks - in terms of scanning, absorptive capacity, diffusion, robustness, and vulnerability to external diffusion, robustness, and vulnerability to external environmentenvironment

DDesignesign: Model or re-wire networks using social and : Model or re-wire networks using social and organizational incentives (based on social network organizational incentives (based on social network research) and network referral systems to enhance research) and network referral systems to enhance evolving and mature communitiesevolving and mature communities

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“Discovery” Problems in Knowledge Networks

IDC found Fortune 500 companies lose $31.5 billion annually due IDC found Fortune 500 companies lose $31.5 billion annually due to rework and the inability to find information. to rework and the inability to find information.

The Delphi Consulting Group found that:The Delphi Consulting Group found that: Only 12 percent of a typical company's knowledge is explicitly

published. Remaining 88 percent is ‘distributed knowledge’, comprised of employees' personal knowledge.

Up to 42 percent of knowledge professionals need to do their jobs comes from other people's brains - in the form of advice, opinions, judgment, or answers. More often than not, much of this exchange does not follow channels displayed in an organizational chart.

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Discovery Challenges

Who knows who?Who knows who?

Who knows what?Who knows what?

Who know who knows who?Who know who knows who?

Who knows who knows what?Who knows who knows what?

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Goal of Discovery – “IKNOW”

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“Diagnosis”: Why Diagnose the Network?

Naturally occurring networks are not Naturally occurring networks are not

always efficient or fully functionalalways efficient or fully functional

Gaps, isolates, lack or difficulty of connectivity

Network measures can be used to Network measures can be used to diagnose network’s vital statisticsdiagnose network’s vital statistics

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Diagnosis Questions How capable at scanning external expertise?

How capable at absorbing expertise from the external network to the internal network?

How efficient at diffusing the external expertise within the internal network?

How robust in a specific area of expertise against disruption?

How vulnerable to being externally brokered?

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Strongest Strongest capacity to capacity to

absorbabsorb

From Diagnosis to “Design”

1.1. Identifying which network links need to Identifying which network links need to be “re-wired” optimize the collective be “re-wired” optimize the collective power of the network.power of the network.

2.2. Identifying the Individual, Identifying the Individual, Organizational and Social Incentives – Organizational and Social Incentives – for members to want to re-wire.for members to want to re-wire.

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Designing CoPs as Small World Networks

Industries with small world network structures are more Industries with small world network structures are more innovative! innovative!

Networks where people spend most of their time Networks where people spend most of their time communicating with one another in a group (“cluster”) communicating with one another in a group (“cluster”) andand spend some time communicating with others spend some time communicating with others outside (“short cuts”) outside (“short cuts”)

Small world networks exhibit high levels of “clustering” Small world networks exhibit high levels of “clustering” and few “shortcuts”and few “shortcuts”

Clusters engender trust and control, maximize Clusters engender trust and control, maximize capability for capability for exploitationexploitation

Shortcuts engender unique combinations of network Shortcuts engender unique combinations of network resources, maximize capacity for resources, maximize capacity for explorationexploration

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“Pre-wired” PackEdge CoP Network

“Re-wired” PackEdge CoP Network

Wiring the PackEdge CoP Network for Success

Increase the likelihood to give and get information to Increase the likelihood to give and get information to the right target and source respectivelythe right target and source respectively

Benefits for CoPBenefits for CoP Increase absorptive capacity from 45.3% to 53.4%Increase absorptive capacity from 45.3% to 53.4% Reduce number of steps for diffusion from 4.3 to 2.6Reduce number of steps for diffusion from 4.3 to 2.6

Costs for CoPCosts for CoP Increase communication links of network leaders from Increase communication links of network leaders from

28 to 38 (~ 150 new links).28 to 38 (~ 150 new links). Increase criticality of network leaders from 26.7 % to Increase criticality of network leaders from 26.7 % to

48.5%48.5%

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Core Research Social Drivers for

Creating & Sustaining Communities



Vodafone-Ericsson “Club” for virtual supply chain management (Vodafone)

Kraft Product Design Teams



Digital Media and Learning(MacArthur Foundation)


World of Warcraft (NSF)

Everquest (NSF, ARI, Sony Online Entertainment)

Second Life (Linden Labs)INFORMS paper by Yun Huang et al

Science Applications

VOSS: Virtual Organizations as Socio-technical Systems (NSF)

CP2R: Collaboration for Preparedness,Response & Recovery (NSF) INFORMS paper by Mengxiao Zhu et al



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Source: http://www.mmogchart.com/

Rise of WoW

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Expertise/Information Retrieval Time One

Expertise/Information Retrieval Time Two

Expertise/Information Retrieval Time Three

Unraveling the “Structural Signatures”

Incentive for creating a WoW link with Incentive for creating a WoW link with someone someone

= -1.55 (cost of creating a link) = -1.55 (cost of creating a link) [Self-interest][Self-interest]

+ 0.55 (benefit of reciprocating) + 0.55 (benefit of reciprocating) [Exchange][Exchange]

+ 0.89 (benefit for being a friend of a friend)+ 0.89 (benefit for being a friend of a friend)

[Balance][Balance]

+ 0.04 (benefit of connecting to an expert) + 0.04 (benefit of connecting to an expert) [Cognition][Cognition]

All coefficients significant at 0.05 level

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Tobacco Research: TobIG Demo

Computational Nanotechnology: nanoHUB Demo

Cyberinfrastructure: CI-Scope Demo

Oncofertility: Onco-IKNOW

Design Examples: Mapping & Enabling Networks in …

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Summary Research on the dynamics of networks is well poised to make a Research on the dynamics of networks is well poised to make a

quantum intellectual leap by facilitating collaboration that quantum intellectual leap by facilitating collaboration that leverages recent advances in:leverages recent advances in:

Theories about the social motivations for creating, maintaining, Theories about the social motivations for creating, maintaining, dissolving and re-creating social network tiesdissolving and re-creating social network ties

Development of cyberinfrastructure/Web 2.0Development of cyberinfrastructure/Web 2.0 provide the provide the technological capability to capture relational metadata needed to technological capability to capture relational metadata needed to more effectively understand (and enable) communities.more effectively understand (and enable) communities.

Exponential random graph modeling techniques to make Exponential random graph modeling techniques to make theoretically grounded network recommendations that go beyond theoretically grounded network recommendations that go beyond the Lovegety and SNIFthe Lovegety and SNIF

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SONIC Team members

Zack JohnsonUndergrad, SONIC

Sanjeev JhaDoctoral candidate, SONIC

Jinling LiResearch Programmer, SONIC

York YaoResearch Programmer, SONIC

Yun HuangPost-doc, SONIC

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Acknowledgements

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noshir contractor jane s. & william j. white professor of behavioral sciences jane s. &...

Documents

networks n social networks

science of networks

communities slide

n n knowledge networks

n n cognitive social

organizational networks

interblog networks

multidimensional networks