Social Networks - Institute of Food Research

Social Networks 

Noshir Contractor 

Jane 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 

nosh@northwestern.edu 

SONIC 

Advancing the Science of Networks 

in Communities

■ 1. Turn on the power and set the MODE button you want 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 

to yours set under the same MODE as yours comes near. 

■ 3. FIND lamp blinks when (someone with) a Lovegety for the opposite 

sex to yours set under some different mode from yours come near. In 

that case, you may try the other MODES to “GET” tuned with 

(him/her) if you like. 

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Agenda 

■ A historical overview of the motivations to view social systems from a networks 

perspective. It will illustrate the wide range of contexts in which which 

network theories and 

methods have advanced our understandings. 

■ Brief introduction to the concepts of social networks, cognitive social networks, 

knowledge networks, cognitive knowledge networks and their relevance relevance 

to 21 st century 

organizational forms. 

■ Introduction to various concepts used in network analysis: actors actors 

and attributes of 

actors, relations and properties of relations as well as two-mode two mode and multidimensional 

networks. 

■ Description of common network metrics at the actor, dyadic, triadic, triadic, 

sub-group, sub group, and 

component level. Computation of the concepts will be illustrated using social network 

analysis software tools. 

■ Strategies for the collection of network data: traditional methods methods 

as well as recent 

approaches for digital harvesting of relational metadata to construct construct 

multidimensional 

networks 

■ Multi-theoretical Multi theoretical multilevel (MTML) model to investigate the dynamics for creating, 

maintaining, dissolving, and reconstituting social networks. 

■ Overview of statistical methods to test MTML models: Introduction Introduction 

to exponential 

random graph modeling techniques to test hypotheses about the structure structure 

and dynamics 

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History 

■ Jacob Moreno introduced the ideas and 

tools of sociometry. NYT, April 3, 1933 

■ In the 1950s, Alex Bavelas founded the 

Group Networks Laboratory at M.I.T to 

study the effectiveness of different 

communication patterns in helping small 

groups of people solve common tasks. 

■ Milgram: Milgram: 

Small World Experiments. Six 

degrees of separation 

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Iconic Representation of the Internet 

Donna Cox & Robert Patterson, 1992 

National Center for Supercomputing Applications 

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Networking in the 90s 

Social Capital & Entrepreneurs 

(Financial Times, 1996) 

Me & Monica 

(Forbes, 1998) 

Six Degrees of Hollywood 

(Newsweek, 1999) 

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Six Degrees of Separation 

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Mohamed Atta’s Network 

21% of connections would have to be eliminated before the network would disintegrate.” 

Source: Business 2.0 December 2001. Six Degrees of Mohamed Atta 

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New Scientist, 

2002 

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Sunday New York Times Magazine, Style & Entertaining Section, Fall 2003 

“At At a party for her new line of Le Sportsac bags, Gwen Stefani and the host, Timothy Schifter, 

play classic sociometric roles as the other guests jockey for degree degree 

centrality” centrality 

William Middleton Popular Mechanics ….. .. wouldn’t wouldn t Moreno have been pleased??? 

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Business 2.0 Magazine 2005 

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Forbes, 2007 

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Network Visualization with Pajek 

Source: Pajek Homepage (http://research.lumeta.com/ches/map/gallery/index.html) 

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Amazon Purchase Network of Books on “Network Theory” 

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Amazon Network of Top Selling Books on “Network Science” 

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Amazon Network of Top Selling Books on “Network Society” 

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Central Rationale 

“Its the relationship, stupid!” 

■ Attribute versus relational explanations 

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Social Network Applications 

■ Occupational mobility 

■ The impact of urbanization on 

individual well-being well being 

■ The world political and economic 

system 

■ Elite decision making 

■ Social support -- Communities 

■ Group problem solving 

■ Diffusion and adoption of 

innovations 

■ Corporate interlocks 

■ Belief systems 

■ Cognition or social perception 

■ Markets 

■ Sales Force Automation in 

Corporate Settings 

■ Sociology of science 

■ Exchange and power 

■ Consensus and social influence 

■ Culture and consensus 

■ Coalition formation 

■ Epidemiology and (in particular) 

HIV transmission 

■ Terrorism 

■ Web 2.0 social networking 

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21 st Century Organizational Forms 

Knowledge Economy 

Globalization 

Strategic Alliances 

Virtual Organizations 

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Agenda 







to 21 st 

century organizational forms. 




networks. 









networks 






and dynamics 

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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. 

■ Knowledge Networks: 

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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 


(webbots, avatars, databases, 

“push” technologies) 

To Human Agent 

Source: Contractor, 2001 

Retrieving from 

knowledge repository 

Publishing to 

knowledge repository 

Human Agent to Human Agent 

Communication 

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COGNITIVE KNOWLEDGE NETWORKS 

Non Human Agent’s 

Perception of Resources 

in a Non Human Agent 

Human Agent’s Perception of 

Provision of Resources in a 


Non Human Agent’s 

Perception of what a Human 

Agent knows 

* 

Human Agent’s Perception of 

What Another Human Agent 

Knows 

* … Why Tivo thinks I am gay and Amazon thinks I am pregnant …. 

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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 

Human to Human 

Interactions and 

Perceptions 

Non Human to 

Human Interactions 

and Perceptions 

Non 

Human 

Agent Y 

Human to Non 



Non Human to Non 



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The capturing of massive amounts of digitalized information about 

human behavior (especially relational behavior) 

+ 

The capacity to manipulate those data 

= 

New insights into collective human behavior 

Source: David Lazer (2007) 

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Agenda 












networks. 









networks 






and dynamics 

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Defining Networks 

& 

Collecting Network Data 

■ Introduction to Network Concepts 

◆ Definition of nodes and relations 

◆ Types of Nodes and relations 

◆ Two-mode Two mode networks 

■ Collection of Network Data 

◆ Data collection strategies 

◆ Ethical issues SONIC 


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Actors 

People 

Subgroups 

Organizations 

Collectives/Aggregates: 

Communities 

Nation-states 

Nation states 

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Relations 

Evaluation of one person by another (for example expressed 

friendship, liking, or respect) 

Transfers of material resources (for example business transactions, 

transactions, 

lending or borrowing things) 

Association or affiliation (for example jointly attending a social social 

event, 

or belonging to the same social club) 

Behavioral interaction (talking together, sending messages) 

Movement between places or statuses (migration, social or physical physical 

mobility) 

Physical connection (a road, river or bridge connecting two points) points) 

Formal relations (for example authority) 

Biological relationship (kinship or descent). 

Sending and Receiving Social and Emotional Support 

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Fundamental Concepts 

Actor: Actor: 

Relational Tie: Tie: 

linkage between a pair of actors 

Dyad: Dyad: 

pair of actors and possible relational ties between them 

Triad: Triad: 

triple of actors and possible relational ties among them 

Subgroup: Subgroup: 

subset of actors 

Group or Actor set: set: 

the collection of all actors on which relational 

linkages are to be measured. 

Relation: Relation: 

the collection of relational ties of a specific kind among 

members of a group is called a relation. relation. 

Social Network: Network: 

consists of a finite set or sets of actors and the 

relation or relations defined on them. 

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What are Networks? 

■ In the abstract, a network is a collection of 

nodes, together with a collection of links 

between them. The links are all of the same 

type reflecting a single social relation. 

■ Examples? 

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Types of Relations 

Nondirectional Relations 

Directional Relations 

Dichotomous Relations 

Signed Relations 

Valued Relations 

Multiple Relations 

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Examples: Non-directed 

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Examples: Directed 

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+ 

+ 

Examples: Signed 

_ 

_ _ 

_ 

+ 

+ 

_ 

+ 

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7 

7 

6 

Examples: Valued 

1 

2 3 

2 

7 

7 

2 

7 

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7 

7 

Examples: Multi-relational 

6 

6 

1 

2 3 

2 

7 

7 

2 

7 

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Two Mode Networks 

❏ Two sets of actors (example: people and 

databases) 

❏ One set of actors, one set of events 

(example: people and voluntary 

organizations) 

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7 

7 

Two-Mode: Actor-Actor 

6 

7 

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7 

Two-Mode: Actor-Event 

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Representing relational content 

■ Network data (Cartwright/Harary 

1956) 

◆ Sociometric data: 

X = population under investigation 

represented in X x X table 

◆ Actor-event Actor event matrix: X x Y table 

◆ Arc/node list: 

ab, ae, ba, bc, bd, ce, dc, ea 

◆ Network graph: 

b 

d 

a 

e 

c 

Board members 

Y 

Companies X 

Actor-event 

matrix 

Companies X 

1 0 1 1 

0 1 1 0 

1 1 1 0 

1 0 1 1 

0 1 1 1 

0 1 1 0 

Companies X 

3 1 3 2 

1 4 4 1 

3 4 6 3 

2 1 3 3 

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Converting Two Mode Networks to 

One Mode Networks 

❏ Post multiplying Actor-Event Actor Event matrix by its 

transpose to get Actor-Actor Actor Actor matrix 

❏ Pre-multiplying Pre multiplying Actor-Event Actor Event matrix by its 

transpose to get Event-Event Event Event matrix 

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Questions & Answers 

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Agenda 












networks. 


sub-group, sub group, 

and component level. Computation of the concepts will be illustrated illustrated 

using social 

network analysis software tools. 





networks 






and dynamics 

of networks.

Starting Data Analysis 

■ Data analysis decisions: 

◆ Symmetrize relationships 

✦ Minimum: only reciprocated ties 

✦ Maximum: at least one tie makes a reciprocal 

relation 

◆ Dichotomize 

✦ 1/0: absent or existing tie 

✦ for alued data -> > find meaningful cut point 

■ Data import: 

◆ Excel -> > import 

◆ Type into UCINet spreadsheet 

◆ Node list 

◆ Network vs. attribute data 

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Measuring Network Properties I 

■ Actor: Isolate, Liaison, Degree Centrality, 

Betweenness Centrality, Closeness 

Centrality, Prominence, Structural Holes 

■ Dyad: Geodesic, Distance, Reachability, 

Structural Equivalence, Regular 

Equivalence 

■ Triad: Transitivity 

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Measuring Network Properties II 

■ Sub-groups: Sub groups: Cliques and Components 

■ Global: Network Density, Network 

Centralization 

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Actor Network Properties I 

Degree: Number of links. For directed links 

◆ Indegree: Popularity, prestige 

◆ Outdegree: Expansiveness 

■ Degree: Number of links. For directed links 

■ Betweenness: Connect people not 

connected to one another: Power broker 

■ Closeness: Connected directly or indirectly 

to others with the shortest distance: 

Grapevine central. 

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Isolate 


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Highest Degree 

Centrality: 6 


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Low 

Closeness 

Centrality 

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Highest Betweenness 

Centrality 

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Centrality Measures (Recap) 

■ Degree Centrality 

◆ Number of incoming and outgoing ties 

(nominations) 

◆ Indicates how well an actor is connected within 

the overall network (individual measure) 

◆ Valued: Indegree/Outdegree; dichotomized: 

number of existing ties 

UCInet: Network Centrality > 

Degree 

■ Betweeness Centrality 

◆ Extent to which an actor lies on the shortest 

path between other (two) other actors (adjusted 

by the number of alternative shortest paths) 

◆ Mediates information, resource flow: liaison or 

gatekeeper 

UCInet: Network Centrality > 

Betweeness 

degree = 7 

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Centrality Measures (Recap) 

■ Closeness centrality 

◆ Number of links/steps along shortest paths from the focal actor to any 

other actor 

◆ Indicator of how quickly an information can reach an actor 

UCInet: Network > Centrality > Closeness 

MEGA Centrality Analysis in UCInet 

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“3D” Strategy for Enhancing 

■ “Discover 

Communities of Practice 

Discover” existing CoP networks – e.g. connecting 

those who have resources with those who need 

resources ““If If only only HP HP knew knew what what HP HP knew”” knew 

Diagnose” existing CoP networks – Exploration 

(scanning, absorptive capacity, and vulnerability) & 

Exploitation (diffusion and robustness) 

■ “Diagnose 

Design” future CoP networks – Identifying and 

Nurturing CoP Network leadership & Rewiring the 

CoP network SONIC 

■ “Design 


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PackEdge CoP Vital Statistics 

■ Exploration 

◆ Scanning: Scanning Access to expertise external to CoP 

◆ Absorption: Absorption: 

Ability to absorb expertise external to CoP 

◆ Vulnerability: Vulnerability: 

Brokered by members external to CoP 

■ Exploitation 

◆ Diffusion: Diffusion: 

Ability to diffuse expertise throughout CoP 

Robustness: : Not relying on few critical CoP members 

to keep things together 

◆ Robustness 

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Most effective 

diffusers

P&G is most 

vulnerable to 

Toyo-Seikan (JP)

Strongest capacity 

to absorb

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Designing CoPs as 

Small World Networks 

■ Industries with small world network structures are more 

innovative! 

◆ Networks where people spend most of their time 

communicating with one another in a group (“cluster ( cluster”) ) and 

spend some time communicating with others outside (“short ( short 

cuts”) cuts ) 

◆ Small world networks exhibit high levels of “clustering clustering” and 

few “shortcuts shortcuts” 

✦ Clusters engender trust and control, maximize capability 

for exploitation 

✦ Shortcuts engender unique combinations of network 

resources, maximize capacity for exploration 

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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 

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% 

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Actor Network Properties III 

■ Structural Hole 

◆ Redundancy/Effective Size: The number of 

alters minus the average degree of alters within 

the ego network, not counting ties to ego). 

◆ Constraint: A measure of the extent to which 

ego is invested in people who are invested in 

other of ego's alters. 

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Structural Holes & Redundancy 

A 

B 

_ 

D 

C 

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Actor Network Properties 

Egocentric network: 

No. of alters: 6 

Degree of alters: 18 

Effective network size: 

6-18/6 = 3 

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Dyadic Network Properties 

■ A path is an alternating sequence of nodes and links, starting and 

ending with a node. For example, A and B might not be directly 

connected, but there might be a path that links them: A--- A---B--- 

---C--- ---D. D. 

◆ The length of a path is defined as the number of links in it. The The 

length of 

the path from A to D above is 3, because there 3 links between them. them. 

The 

path from A to B has length 1. 

■ The shortest path between any two points is called a geodesic. geodesic. 

■ The distance between any pair of nodes is defined as the length of a 

geodesic from one to the other. In other words, the distance is the 

number of links in the shortest path between the nodes. 

■ If there exists a path of any length that connects a pair of points, points, 

they 

are said to be reachable from each other. 

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Dyadic Network Properties 

Geodesic 

Path distance: 4 

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■ Distance 

Path Distance 

◆ Calculates the average distance to reach every actor in the network network 

◆ Shorter distance = fast reach, short paths, high connectivity 

In UCInet: Network > Cohesion > Distance 

Short average distance Long average distance 

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A and B are 

structurally 

equivalent 

Structural Equivalence 

A 

B 

D 

E 

C 

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Triadic Network Properties 

■ Transitivity 

A 

B 

+ 

C SONIC 


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Triadic Network Properties 

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Source Broker Target 

Brokerage Roles 

■ Coordinator (Bonding ( Bonding) 

◆ All in same group 

Consultant 

All in same group 

■ Consultant 

◆ Same source and target, 

different broker 

■ Gatekeeper 

◆ Same broker and target, 

different source 

■ Representative 

◆ Same broker and source, 

different target 

■ Liaison (Bridging ( Bridging) 

◆ All in different groups 

Colors = Different Organizational Types 

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Mexican Immigrant Community Project 

■ Identify cultural, artistic, and networking capacities and 

assets of post-NAFTA post NAFTA Mexican immigrant community. 

■ Analyze how these capacities buffer challenges or 

obstacles faced by migrants as they traverse the 

transnational landscape. 

■ Investigate how cultural knowledge is distributed 

throughout transnational migrant community 

■ Understand new forms, new applications of existing 

forms, and emerging hybrids to explore community 

formation, community building strategies, and creative 

potential of migrants. 

In partnership with the Center for 

Cultural Understanding & Change @ 

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Three Chicago Communities 

Aurora 

Albany Park 

South Chicago 

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Ethnographers use Tablet PCs to 

learn from community members 

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Mapping Cultural & Network Assets in 

Chicago’s Mexican Immigrant Community 

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Mapping Arts and Culture Activities in 


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Re-Mapping Arts and Culture Activities in 


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Patterns of Liaison Activity I 

■ Social Service Organizations => 

◆ Arts and Cultural Groups => 

◆ Community Centers => 

◆ Home Town Assoc => 

Line Thickness = Number of Relationships 

Sources Broker Targets

Patterns of Liaison Activity II 

◆ Cultural Institutions 

◆ Businesses 

◆ Mass Media 

◆ Limited evidence that Labor 

Organizations, Churches, and Schools 

play liaison roles based on these data 

Line Thickness = Number of Relationships 

Sources Broker Targets 

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Sub Groups: 

Components & Cliques 

■ A maximal subset of actors that are 

mutually reachable is called a component. component. 

■ Cliques are maximal subsets of actors that 

are completely connected: all actors are 

connected with all others. 

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Cliques & Components 

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Global Network Properties 

■ Network Density: The density of a binary 

network is the total number of ties divided 

by the total number of possible ties. For a 

valued network it is the total of all values 

divided by the number of possible ties. In 

this case the density gives the average 

value. 

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■ Density 

Network Density 

◆ Overall level of connection within a network 

◆ Relative measure: Total number of ties/max. possible number of ties ties 

In UCInet: Network > Cohesion > Density 

Low density (25%) 

Average dist. = 2.27 

High density (39%) 

Average dist. = 1.79 

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Global Network Properties II 

■Network Network Centralization 

The extent to which some actors in the 

network have a much higher (or lower) actor 

network centrality than other actors in the 

network. 

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Comparing Relations 

■ The extent to which two relations – social 

communication and task related 

communication – overlap. 

■ Quadratic Assignment Procedure: The 

probability that the overlap is much more 

than is likely to occur by chance – that is, 

by alternative permutations of the two 

relations. 

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Agenda 












networks. 








multidimensional networks 






and dynamics 

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Assembling a social network 

■ Surveys 

◆ Collect perceptions of interactions 

◆ List of names or free recall 

◆ Free vs. fixed choices 

◆ Ratings vs. complete rankings 

■ Observations 

Data collection 

◆ Face-to Face to-Face Face interactions: Who talks to whom at a party? 

◆ Who answers to what kinds of requests on a list server? 

■ Interviews 

◆ Face-to Face to-face, face, or telephone 

◆ Example: Snowball principle: Who else is important in this network? network? 

■ Indirect data 

◆ Archival records: past political interactions, co-authorship, co authorship, court records, … 

◆ More reliable: without social ranking 

◆ Avoid the risk of inaccurate recollections of respondent 

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Data Collection 

There are additional ways in which social network 

data can be gathered. These techniques include: 

�� Experiments 

�� Ego-centered 

Ego centered 

�� Small World 

�� Diaries 

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Questionnaire formats 

Question formats that can be used in a 

questionnaire include: 

Roster vs. Free Recall 

Free vs. Fixed Choice 

Ratings vs. Complete Rankings 

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Multidimensional Networks in Web 2.0 

Multiple Types of Nodes and Multiple Types of Relationships 

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Its all about “Relational Metadata” 

■ Technologies that “capture capture” communities’ communities relational meta-data meta data (Pingback and 

trackback in interblog networks, blogrolls, data provenance) 

■ Technologies to “tag tag” communities’ communities relational metadata (from Dublin Core 

taxonomies to folksonomies (‘wisdom ( wisdom of crowds’) crowds ) like 

◆ Tagging photos (Flickr) 

◆ Tagging images (ESP) 

◆ Tagging blogs (Technorati) 

◆ Tagging news stories (digg) 

◆ Social bookmarking (del.icio.us) 

◆ 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 manifest” communities’ communities relational metadata (Tagclouds, 

Recommender systems, Rating/Reputation systems, ISI’s ISI s HistCite, Network 

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Text mining 

Digital Harvesting 

CATPAC UBERLINK 

Web Crawling 

NETDRAW 

Analyses and Visualizations 

http://iknowinc.com/iknow/sb_digital_forum/www/iknow.cgi 

Web of 

Science 

Citation 

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Issue Crawler (govcom.org) 

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8/29 

8/28 

8/30 

8/27 

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

Hurricane Katrina 2005 

8/31 

8/26 

8/25 

8/24 

8/23 

Formed: Aug 23, 2005 

Dissipated: Aug 31, 2005 

Highest wind: 175 mph 

Lowest press: 902 mbar 

Damages: $81.2 Billion 

Fatalities: >1,836 

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SITREP Content 

■ Basic Format / Information 

1. Situation (What, Where, and When) 

2. Action in Progress 

3. Action Planned 

4. Probable Support Requirements and/or 

Support Available 

5. Other 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 New 

Orleans. It was considered a Category 5 Hurricane, which brings winds of 

over 155mph and storm surge of 18 feet above normal. Massive property damage 

has 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 during 

the 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's 

Office.* * 

*Additional Assistance Requested:* Type III teams, consisting of Operations, 

Plans, and Logistics personnel (two individuals for each area). These teams 

could deploy to Alabama, Louisiana, and/or Mississippi. Teams will be 

at either working the State or Parish/County EOCs. 

· · · 

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Automatic Coding 

■ T2K – The Text to Knowledge application 

environment is a rapid, flexible data mining 

and machine learning system 

■ Automated processing is done through 

creating itineraries that combine processing 

modules into a workflow 

■ Developed by the Automated Learning Group 

at NCSA 

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

Florida is the Topic 

of the Conversation 

Petroleum Network 

formed Early 

KY 

LA 

NO 

FEMA 

TX 

SAL 

ARC 

Shelter 

FL 

AL 

Gov Bush 

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Time Slice 1 to 2 

KY 

GA 

LA 

NO 

FEMA 

TX 

Military 

SAL 

ARC 

Shelter 

FP&L 

FL 

AL 

Gov Bush 

Power 

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

FEMA 

Gov Bush 

ARC 

LA 

NO 

GA 

Military 

TX 

FL 

SAL 

MS 

Shelter 

FP&L 

Power 

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NO 

FEMA 

Military 

GA 

Gov Bush 

ARC 

LA 

TX 

FL 

SAL 

MS 

Shelter 

FP&L 

Power NC 

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

Gov Bush 

GA 

FEMA 

ARC 

FP&L 

MS 

Shelter 

FL 

TX 

LA 

Power 

Military 

NO 

NC 

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Gov Bush 

GA 

AL Power 

FEMA 

ARC 

FP&L 

AL 

MS 

Shelter 

FL 

TX 

National Guard 

LA 

Power 

Military 

NO 

NC 

S & R 

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

ARC 

NC 

TX 

FP&L 

AL Power 

GA 

AL 

FEMA 

Shelter 

FL 


Power 

LA 

MS 

NO 

S & R 

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ARC 


NC 

TX 

FP&L 

AL Power 

GA 

AL 

FEMA 

Shelter 

FL 


Power 

LA 

MS 

NO 

S & R 

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

AL Power 

Power 

NC 

S & R 

AL 

FL 


TX 

GA 

Shelter 

ARC 

NO 

MS LA 

FEMA 

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AL Power 


Power 

S & R 

AL 

FL 


TX 

GA 

Shelter 

ARC 

NO 

MS LA 

FEMA 

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

Outages 

AL Power 

Shelter 

FL 

MS 

TX 

AL 


NO 

LA 

S & R 

FEMA 

GA 

ARC 

Urban S & R 

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Betweeness Rank 

Emergence of Key Organizations and Places 

■ “Hurricane Hurricane Katrina” Katrina is the top ranked betweeness centrality during all time 

slices 

■ “Florida Florida” starts ranked as # 2 and settles around # 15 rank 

■ “American American Red Cross” Cross starts ranked in the 200s and moves up to the teens 

■ “FEMA FEMA” starts ranked in the 20s, moves to the teens, and ends in the 60s 60s 

■ “New New Orleans” Orleans Starts in the 200s, moves to #5, and ends in the teens 

1 2 3 4 5 6 7 

0 

5 0 

1 0 0 

1 5 0 

2 0 0 

2 5 0 

3 0 0 

T i m e S l i c e 

H u r r ic a n e K a t r in a F lo r id a N e w O r le a n s F E M A A m e r ic a n R e d C r o s s 

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Rank 

250 

200 

150 

100 

50 

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 

Betweeness Centrality 

0 

1 2 3 4 5 6 7 

Time Slice 

American Red Cross FEMA 

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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Oncofertility Consortium Co-authorship Network 

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Oncofertility Consortium Author’s Co-citation Network 

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Oncofertility Consortium Citation Network 

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Oncofertility Consortium Co-author’s Institutions Network 

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CLEANER Cybercommunity 

Collaborative ollaborative 

Large arge-Scale Scale 

Engineering ngineering 

Analysis nalysis 

Network etwork for 

Environmental 

nvironmental 

Research esearch 

The first cybercommunity 

implementing CI-KNOW 

http://cleaner.ncsa.uiuc.edu/

Hydrologic 

Information 

System 

Status Report 

CLEANER Community: A multidimensional network 

River Monitoring 

Systems 

DOWNLOADS 

HUDSON 

RIVER Hydro 

DATA 

Contains 

as 

Keyword 

AUTHOR OF 

Searches for 

Keyword 

C-U County 

Hydrologic 

Dataset 

David 

Streamflow 

Analyst 

USES 

Annie 

AFFILIATED WITH 

CHATS WITH 

Mary 

EXPERT 

IN 

NCSA 

watersheds

CI-KNOW: Harvesting the online 

community’s relational meta-data 

Linking all 

data together 

Cybercommunity 

Resources 

Cyberinfrastructure 

Use 

1. Algorithms to 

generate Network 

Referrals 


create External Network 

Maps 

Resources 


compute Network 

Diagnostics 

INPUTS 

Using Tools to 

Analyze 

Datasets 

Users’ Profiles 

Documents 

Collaboration Tools 

Datasets Generating 

a Multi- 

Analysis Tools 

Dimensional 

network 

Downloading 

Presentations 

User activity logs related 

Bibliographic DBs 

to Personal cyberinfrastructure Websites 

Organizational 

Network 

Websites 

Project Analysis Websites 

Patent Databases 

PROCESSES 

Network 

Forum 

Maps 

Using Chats, 

Network 

Referrals 

Network 

Diagnostics 

OUTPUTS

CI-KNOW: Harvesting the online 

community’s relational meta-data 

Cybercommunity 

Resources 

Cyberinfrastructure 

Use 

External 

Resources 

INPUTS 

Generating 

a Multi- 

Dimensional 

network 

1. Who to contact for 

what topic 

2. What tools to use 

for what data 

3. 

1. 

What 

What 

dataset 

nodes are 

to 

analyze 

important 

for 

for 

what 

what 

concepts 

relations 

4. 

2. 

What 

The amount 

papers Network of 

to 

read 

scanning, 

for Analysis what 

keywords 

absorption, 

diffusion, 

robustness, 

vulnerability in a 

network 

PROCESSES 

Network 

Maps 

Network 

Referrals 

Network 

Diagnostics 

OUTPUTS

Design Examples: 

Mapping & Enabling Networks in … 

Tobacco Research: TobIG Demo 

Computational Nanotechnology: nanoHUB Demo 

Cyberinfrastructure: CI-Scope Demo 

Oncofertility: Onco-IKNOW 

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Agenda 












networks. 









networks 






and dynamics 


WHY DO WE 

CREATE, 

MAINTAIN, 

DISSOLVE, AND 

RECONSTITUTE OUR 

COMMUNICATION 

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- 

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 homophily 

■ Theories of proximity 

Theories of proximity 

■ Theories of co- 

evolution 

Sources: 

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. 

Monge, P. R. & Contractor, N. S. (2003). Theories of Communication Networks. New York: Oxford 

University Press.

Seven Families of Social Science Theories 

and their Theoretical Mechanisms 

Theory Families Theoretical Mechanisms 

Theories of Self-Interest Self Interest Maximization of individual benefits 

Social Capital Profit from Investment opportunities 

Structural Holes Control of information flow 

Transaction Costs Cost minimization 

Collective Action Joint value maximization 

Public Goods Theory Inducements to contribute 

Critical Mass Theory People with resources & interests 

Cognitive Theories Cognitive Mechanisms leading to: 

Semantic/knowledge Networks Shared interpretations 

Cognitive social structures Similarity in perceptual structures 

Cognitive Consistency Maintain consistent cognitions 

Balance Theory Drive to avoid imbalance 

Cognitive Dissonance Drive to reduce dissonance

Selected Families of Social Science Theories 

and their Theoretical Mechanisms, Con’t. 


Contagion Theories Exposure to contact leading to infection: 

Social Information Processing Social Influence 

Social Learning Theory Imitation and modeling 

Institutional Theory Mimetic behavior 

Structural Theory of Action Similarity positions in structure & roles 

Exchange and Dependency Theories Exchange of valued resources 

Social Exchange Equality of exchange 

Resource Dependency Inequality of exchange 

Network Exchange Complex calculi for exchange balance 

Homophily & Proximity Choices based on similarity 

Social Comparison Theory Communicate with comparable others 

Social Identity Theory Choose others based on own group identity 

Physical and Electronic Proximity Influence of distance and accessibility

Selected Families of Social Science Theories 

and their Theoretical Mechanisms, Con’t. 


Theories of Network Evolution Variation, selection, retention 

Organizational Ecology Competition for scarce resources 

Kauffman’s Kauffman s NK(C) Model Network density and complexity 

.

CMDR Structuring Processes 

Exogenous 

attributes 

of actors 

Exogenous 

relations 

in the network 

Structure of 

the network 

Endogenous mechanisms

Network is structured by itself 

■Actor Actor level 

■Dyad Dyad level 

■Triad Triad level 

■Subgroup Subgroup level 

■Global Global level 

(Endogenous)

Endogenous Actor Level: 

Theory of Structural Holes 

- 

+

Endogenous Dyad Level: 

Exchange Theory 

- 

+

Endogenous Triad Level: 

Theories of Balance 

+ 

-

Endogenous Global Level 

Collective Action Theory 

- 

+

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

INTEGRATING MULTIPLE THEORIES 

AT MULTIPLE LEVELS 

■ Multiple complementary and contradictory 

theories 

◆ Theories of Balance 

◆ Theories of Structural Holes 

■ Multiple levels of analysis 

◆ Individual: Individual: 

Theories of Structural Holes 

◆ Dyad: Dyad: 

Social Exchange Theory 

◆ Triad: Triad: 


◆ Subgroup: Subgroup: 

Theories of Cohesion 

◆ Global: Global: 

Theories of Collective Action

B 

C 

“Structural signatures” of MTML 

Theories of Self interest Theories of Exchange 

A 

D 

Theories of Collective Action 

+ 

E 

F 

B 

C 

B 

C 

- 

+ 

G o v e rn m e n t 

In d u s try 

A 

D 

A 

D 

- 

+ 

E 

F 

F 

E 

B 

C 

+ 


B 

C 

Novice 

Expert 

A 

D 

A 

D 

- + 

Theories of Homophily Theories of Cognition 

- 

E 

E 

F 

F

A large array of theoretical mechanisms that offer 

contradictory and complementary explanations 

Collective Action 

Exchange and Dependency 

Contagion 

Theories of Cognition 

Self Interest 

Homophily 

Proximity 

Coevolutionary Theory 

Theories of Cohesion 

Balance 

Source: Monge & Contractor, 2000

A contextual “meta-theory” of 

social drivers for creating, sustaining, 

and dissolving networks 

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 -- + +

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) 

Societal Justice Applications 

Cultural & Networks Assets 

In Immigrant Communities 

(Rockefeller Program on 

Culture & Creativity) 

Economic Resilience NGO 

Community 

(Rockefeller Program on Working 

Communities) 

Core Research 

Social Drivers for 

Creating & Sustaining 

Communities 

Business Applications 

PackEdge Community of 

Practice (P&G) 

Vodafone-Ericsson “Club” 

for virtual supply chain 

management (Vodafone) 

Entertainment Applications 

World of Warcraft (NSF) 

Everquest (NSF, Sony Online 

Entertainment)

Contextualizing Goals of Communities 


Emergency Response 

Community 

+ + + 

WoW Gaming Community + + + 

Mexican Immigrant 

Community 

+ + 

PackEdge Communities of 

Practice 

+ + + 


Community 

Tobacco Surveillance, 

+ + 


Community 

+ + 

Environmental Engineering 

Community 

+ + + 

Challenges of empirically testing, extending, and exploring theories about 

communities … until recently

Agenda 












networks. 









networks 






and dynamics 


Integrating exogenous and endogenous 

processes based on multiple theories at 

multiple levels leads to many possible 

realizations of the network

Unraveling the “Structural Signatures” 

p*/Exponential Random Graph Modeling (ERGM) 

“A statistical MRI” 

■ 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.

Applying p* Framework 

■ The statistical estimates of p* /ERGM 

parameters indicate whether network 

realizations with the theoretically 

hypothesized properties have significantly 

large probabilities of being observed in sub- 

graphs of the network data collected.

Empirical Illustration 

Co-evolution of knowledge networks 

and 21 st century organizational forms 

■ NSF KDI Initiative 1999-04. 1999 04. PI: Noshir 

Contractor, University of Illinois. 

■ Co-P.I.s: Co P.I.s: Bar, Fulk, Hollingshead, Monge 

(USC), Kunz, Levitt (Stanford), Carley 

(CMU), Wasserman (Indiana). 

■ Three dozen industry partners (global, profit, 

non-profit): non profit): 

◆ Boeing, 3M, NASA, Fiat, U.S. Army, American 

Bar Association, European Union Project Team, 

Pew Internet Project, etc.

MTML analysis of information 

retrieval and allocation 

■ Why do we create information retrieval and 

allocation links with other human or non-human non human 

agents (e.g., Intranets, knowledge repositories)? 

■Multiple Multiple theories: Transactive Memory, Public 

Goods, Social Exchange, Proximity, Contagion, 

Inertial Social Factors 

■Multiple Multiple levels: Actor, Dyad, Global 

UIUC Team Engineering Collaboratory: 

UIUC Team Engineering Collaboratory: David 

Brandon,Roberto Dandi, Meikuan Huang,Ed Palazzolo, 

Cataldo “Dino” Ruta, Vandana Singh, and Chunke Su)

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 

Transactive Memory 

◆ Perception of Other’s Other s 

Knowledge 

◆ Communication to 

Allocate Information 

Social Exchange 

- Retrieval by coworkers on 

other topics 

Proximity 

-Work in the same location

Pulling Theories Together: p* 

■ Using a multivariate p* procedure, procedure, 

we combined 

the primary relations from each of the theories into 

a single analysis 

■ This framework allows us to test for the additive 

predictability of each theory as well as interaction 

effects between the theories 

■ Focus for analysis: 

Predicting a tie between two actors for 

information retrieval based on multiple theories

Multi-theoretical p* 

Theoretical Predictors of CRI 

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

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 


Network (NSF, NIH, CDC) 

Societal Justice Applications 

Cultural & Networks Assets 

In Immigrant Communities 

(Rockefeller Program on 

Culture & Creativity) 


Community 

(Rockefeller Program on Working 

Communities) 

Core Research 

Social Drivers for 

Creating & Sustaining 

Communities 

Business Applications 

PackEdge Community of 

Practice (P&G) 

Vodafone-Ericsson “Club” 

for virtual supply chain 

management (Vodafone) 

Entertainment Applications 

World of Warcraft (NSF) 

Everquest (NSF, Sony Online 

Entertainment)

Rise of WoW 

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

Expertise/Information Retrieval Time One

Expertise/Information Retrieval Time Two

Expertise/Information Retrieval Time Three

Contextualizing Goals of Networks 



Community 

+ + + 



Community 

+ + 


Practice 

+ + + 


Community 


+ + 


Community 

+ + 


Community 

+ + +

Mapping Goals to Theories: WoW Gaming Community 

Exploitation - Collective Action, Cognition, Exchange 

Bonding - Balance, Exchange, Homophily, Proximity 

Swarming - Collective Action, Cognition, Proximity 



Community 

+ + + 



Community 

+ + 


Practice 

+ + + 


Community 


+ + 


Community 

+ + 


Community 

+ + + 


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 -- + +

WoW Evolution of Network Structure 

■ Guild members tend NOT to ask 

for advice from other guild 

members over time. 

■ Guild members tend to 

reciprocate advice ties with other 

members over time. (social 

exchange) 

■ Guild members tend to get 

advice from the person who 

gives advice to the person they 

ask for advice over time. 

(balance, transitivity) 

I J I X J 

I J I J 

I 

K 

J 

Time 1 Time 2 

I 

K 

J

Unraveling the 

“Structural Signatures” 

■ Incentive for creating a WoW link with 

someone 

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

[Self interest] 

+ 0.55 (benefit of reciprocating) [Exchange] 

+ 0.89 (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

Summary 

■ Research on the dynamics of networks is well poised to 

make a quantum leap in facilitating multi/inter/transdisciplinary 

collaboration in STEM research by leveraging 

recent advances in: 

◆ Theories about the social and organizational incentives 

for creating, maintaining, dissolving and re-creating 

social and knowledge network ties 

◆ Exponential random graph modeling techniques to 

statistically model and make theoretically grounded 

network recommendations 

◆ Development of cyberinfrastructure/Web 2.0 provide 

the technological capability that go beyond SNIF SONIC 


in Communities

Online Resources 

■ GraphViz (Open Source Graph Drawing Program) ttp://www.graphviz.org/ 

■ iKnow Inquiring Knowledge Networks on the Web: 

http://www.spcomm.uiuc.edu/Projects/TECLAB/IKNOW/ 

■ KrackPlot: KrackPlot: 

http://www.andrew.cmu.edu/user/krack/krackplot/krackindex.htmlNetVis http://www.andrew.cmu.edu/user/krack/krackplot/krackindex.htmlNetVis 

Module 

Dynamic Visualization of Social Networks (MIT): 

http://www.netvis.org/resources.php 

■ Mage - 3D vector display program which shows "kinemage" graphics – 

(http://kinemage.biochem.duke.edu/kinemage/kinemage.php) 

■ Netdraw: Netdraw: 

http://www.analytictech.com/downloadnd.htm 

■ Pajek Program for Large Network Analysis: http://vlado.fmf.uni- 

lj.si/pub/networks/pajek/ 

■ UCInet: UCInet: 

http://www.analytictech.com/default.htm 

Yahoo usergroup: http://groups.yahoo.com/group/ucinet 

■ Visone – analysis and visualization of social networks http://www.visone.de 

■ Link collection of network visualization tools (especially for Internet Internet 

data 

visualization): http://www.caida.org/projects/internetatlas/viz/viztools.html 

http://www.caida.org/projects/internetatlas/viz/ viztools.html

Social Network Analysis Courses 

■ Center for Computational Analysis of Social and Organizational Systems Systems 

(CASOS) 

Summer Institute directed by Kathleen M. Carley: The purpose of this institute is to 

provide an intense and hands-on hands on introduction to dynamic network analysis and 

computational organization theory (Quote from CASOS website). Recent Recent 

instructors: 

Carley, Krackhardt, Borgatti. 

■ Essex Summer School in Social Science Data Analysis and Collection (UK) (UK 

Introduction to Social Network Analysis alternately taught by Steve Steve 

Borgatti and Martin 

Everett (full syllabus including links to papers and other valuable valuable 

online resource) at the 

University of Essex in England. 

Advanced Social Network Analysis taught by John Skvoretz at the University of Essex in 

England. 

■ ICPSR 

Interuniversity Consortium for Political and Social Research (University (University 

of Michigan, 

Ann Arbor) 

Summer Program in Quantitative Methods of Social Research 

Social Network Analysis: Theories and Methods, Stan Wasserman and and 

Bernice 

Pescosolido (University of Indiana, Bloomington) 

Social Network Analysis: An Introduction, Stan Wasserman (Univerisity (Univerisity 

of Indiana, 

Bloomington) 

■ PolNet 

Summer School on the Analysis of Political and Managerial Networks Networks 

Basic introduction into Social Network Analysis using UCInet and Visone, co-organized 

co organized 

by the University of Konstanz (Germany) and Tilburg University (The ( The Netherlands). 

■ Additional classes and colloquia in Social Networks and Network Analysis: 

For a comprehensive list of courses including syllabi see INSNA website.

Acknowledgements 

SONIC 


in Communities

Social Networks - Institute of Food Research

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