Introduction to Social Network Analysis

Network Analysis in Two Parts

(with an Introduction)Patti Anklam

Columbia IKNS Unit 4April 2016

Introduction: Graph Theory Put to Work

Columbia IKNS Residency April 2016

Origins of Network Study

• Graph theory

– Euler, the seven bridges of Königsberg (1736)

• Sociometry

– Jacob Moreno, Hudson Training School for Girls (1932)

3


Symposium on Social Networks: Dartmouth, 1975

http://eclectic.ss.uci.edu/~drwhite/Networks/MSSB1975.html


2007

Network Theory Reaches the Business World

20022002

2002

2003

2004

2004

52005

2009

2009

2002

2002


Organizational Networks

6

Source: MWH Global, Vic Gulas


Disease and Health

7


Networks of Companies

8

Source: Laurie Lock Lee, http://www.optimice.com.au

Equipment Manufacturers

Systems integrators


https://kumu.io/UnLtdUSA/austin-social-entrepreneurship

People and Companies

9

Austin Social Entrepreneurship


Mapping Ideas and Topics

10http://www.smrfoundation.org/2009/09/12/networks-in-the-news-news-dots-on-slate/


Showing Affiliations

11


The Premise: Networks Matter

• Social Capital– People with stronger personal networks are

more productive, happier, and better performers

– Companies who know how to manage alliances are more flexible, adaptive and resilient

– Our personal health and well-being is often tied to our social networks

• Making Sense– Once we have the distinction “network”

then we can use our knowledge of the networks we live in to make sense

12


The Opportunity: Leverage the Science

13

• Graph theory provided the underlying math and science to help us make sense of the network structure

• The structure of a network provides insights into network patterns:

• About the structure of the network

• About people in the network

• Once you understand the structure, you can make decisions about how to manage the network’s context – this is Net Work

I’ve become convinced that understanding how networks work is an essential 21st

century literacy.

Howard Rheingold


The Importance of Understanding Networks

15

Burt, Ronald S. and Don Ronchi, Teaching executives to see social capital: Results from a field experiment http://faculty.chicagobooth.edu/ronald.burt/research/files/TESSC.pdf


The Two Parts

―The language of networks

―Networks in organizations

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Social Network Analysis: Cases and Concepts

Mapping Networks: Tools

Social Network Analysis: Cases and Concepts

http://www.dftdigest.com/images/Spyglass.jpg


The Business Case

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Management Practice Business Need

Talent Management Finding the natural leaders in the organization

Innovation Identify boundary crossersEnsure organization has access to new ideas

Collaboration Finding gaps in knowledge flow within groups, or across organizations or geographiesMonitor or measure changes

Knowledge management

Identify and retain vital expertiseMonitor or measure changes in k. exchange

Organizational Change and Development

Identifying opinion leaders for change management initiatives or during integration following mergers and acquisitions

OrganizationalPerformance

Diagnosing cohesion among team membersand targeting critical connections for improvement


Rob Cross’s Classic Case: A Performance Issue

19

From: The Organizational Network Fieldbook, Rob Cross et al, Jossey-Bass 2010

Where are the most frequent information flows?

Formal Structure Informal Structure


A Classic Case

20




A Classic Case

From: The Hidden Power of Social Networks, Rob Cross and Andrew Parker, Harvard Business School Press, 200421




A Classic Case

22




A Classic Case

23




What Factors Influence Connections?

• Homophily: Birds of a feather, flock together

• Propinquity: Those close by, form a tie

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Elements in a Network Diagram

25

• A network diagram shows a collection of entities (nodes) linked by a type of relationship (represented by an edge) Nodes

Edges

Node: Vertex, AlterEdge: Tie, connection, linkNetwork diagram: graph, sociogram

Synonyms


Nodes Have Attributes

• Information from survey and/or HR data*:

– Organizational unit

– Job title/role

– Location

– Expertise

– Job level

– Age

– Gender

• Additional attributes may come from the survey data itself

26*within the bounds of what is legal and appropriate


About Edges

27

• Edges (and the graph as a whole) are either:• Undirected (merely connected)• Directed (edges go “from-to”)

• Reciprocity sometimes matters

Undirected

Node: Vertex, AlterEdge: Tie, connection, linkNetwork diagram: graph, sociogram

Synonyms

Directed

Reciprocal


Edges Define the Shape of the Network

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• In a survey we might ask:• “I get information from this

person”• “I socialize with this person”• “I think this person is an expert”• “I go to this person when I have an

idea I want to explore”

• In looking at data, we might want to find out:• People who responded to each

others’ emails• People who attended the same

meetings or who appeared at the same event – or in the same scene!

In creating a social network diagram, we define what we mean by an edge


Weights and Tie Strength

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• Edges may have values, or weights, associated with them. For example the difference between:

• Exchanging a few emails

• Being best friends

• The strength between two nodes may also reflected having multiple relationships:

• Exchange information frequently AND

• Socialize AND

• Share trusted informationNode: Vertex, AlterEdge: Tie, connection, linkNetwork diagram: graph, sociogram

Synonyms


Edge Data from Surveys

30

• Surveys:

– Edge data may or may not be weighted

– People may answer questions about everyone in the network or nominate people they communicate with, seek advice from, etc.

• Weighted questions may denote frequency or some kind of strength

Cases

http://www.dftdigest.com/images/Spyglass.jpg


How Are We Managing Expertise?

Acknowledged Expert

Colleague

Questions visualized on the map:

1. Whom do you turn to for professional

advice regarding your daily work?

2. Who is the most acknowledged professional in your field?

Source: Maven7/Orgmapper



Accessible knowledgeAcknowledged Expert

Colleague

Group with no

direct access to a

knowledge center





Non-accessible

knowledge




Acknowledged Expert

Colleague

Cluster with no

direct access to a

knowledge center







California Computer

35

From “Informal Networks: The Company”David Krackhardt and Jeffrey R. HansonHBR, 1993

CEO Leers must choose someone to lead a strategic task force.

Bair

Stewart

Ruiz

O'HaraS/W Applications

Harris

Benson

Fleming

Church

Martin

Lee

Wilson

Swinney

Huberman

Fiola

CalderField Design

Muller

Jules

Baker

Daven

Thomas

Zanados

LangICT

Huttle

Atkins

Kibler

SternData Control

LeersCEO


California Computer

36

From “Informal Networks: The Company”David Krackhardt and Jeffrey R. HansonHBR, 1993

CEO Leers must choose someone to lead a strategic task force.

Bair

Stewart

Ruiz

O'HaraS/W Applications

Harris

Benson

Fleming

Church

Martin

Lee

Wilson

Swinney

Huberman

Fiola

CalderField Design

Muller

Jules

Baker

Daven

Thomas

Zanados

LangICT

Huttle

Atkins

Kibler

SternData Control

LeersCEO


Was Harris a Good Choice?

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Whom do you go to for help or advice?

Field Design

Data Control Systems

Software Applications

CEO

ICT


Was Harris a Good Choice?

38

Whom do you go to for help or advice?

Field Design

Data Control Systems

Software Applications

CEO

ICT


The Question of Trust

39

Whom would you trust to keep in confidence your concerns about a work-related issue?



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41



Network Patterns

Multi-Hub

Clustered Core/Periphery

42

Hub and Spoke


Core/Periphery

43

Core

Periphery

StructuralHole

Isolates


It’s all about Questions

44

Patterns provide insights that provoke good questions.

Full stop.


• Look at the whole network and its components

Network Analysis Also Provides Metrics

• Look at positions of individuals in the network

Centrality Metrics

Structural (Network) Metrics

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Structural Metrics

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• Common measures:

–Density of interactions

–Distance (average degree of separation)

–Diversity

–Communities, or groups

–Centralization

• Good for comparing questions, groups within networks or for comparing changes in a network over time

Look at the whole network and its components


The Metrics: Density

47

Density. Data provides the percentage of information-getting relationships that exist out of the possible number that could exist. It is not a goal to have 100%, but to target the junctures where improved collaboration could have a business benefit.

Percent of connections that exist out of the total possible

Low Density

High Density


Impact on Business of Connectivity

• Bank management was trying to understand the differences across branches in sales at credit and deposit figures

• Using network analysis, the bank was able to understand where to direct mentoring and “best practice” exchanges across banks

48

Figures show the performance differences in bank branches based on the density of their relationships

Total credit / person

Total deposit / person

Low density

branches

High density

branches

Low density

branches

High density

branches



Metrics help reveal diversity within networks

SmA Ops PL A PL B PL C LgA

10 5 8 8 9 10

Small Accounts 72% 2% 11% 0% 2% 5%

Operations 4% 85% 10% 5% 7% 12%

Product Line A 8% 3% 77% 0% 1% 4%

Product Line B 0% 13% 2% 73% 0% 17%

Product Line C 2% 16% 1% 3% 54% 17%

Large Accounts 2% 18% 5% 16% 12% 73%

Density. Data provides the percentage of information-getting relationships that exist out of the possible number that could exist. It is not a goal to have 100%, but to target the junctures where improved collaboration could have a business benefit.

The diagonal shows the interconnectivity among groups in the organization

Off-diagonal, the metrics illustrate the extent to which people are reaching across organizational boundaries

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Tracking Metrics Over Time

50

2010

2011Year # Density Degree

2009 55 2.2% 1.2

2010 90 2.7% 2.4

2011 85 5.3% 4.5

2012 82 8% 6.88

2009

2012


Structural Metrics: Distance

51

Maximum number of steps to get from one node to another: 12Average number of steps: 5


Centrality Metrics: Degree

52Based on: https://plus.google.com/+DaveGray/posts/CQRVeKEsUvF

Raw number of connections (undirected network)

6

7

10

Average Degree: 3.28


Centrality Metrics: In-Degree and Out-Degree


Number of in-coming and out-going connections

Outdegree = 7

Indegree = 5


Centrality Metrics: Betweenness


How many paths does a single node lie on?

855

1080

785

793


Centrality Metrics: Betweenness

Highest Bee-tweenness?

https://www.timeshighereducation.com/sites/default/files/styles/the_breaking_news_image_style/public/bees_teamwork.jpgh/t: Valdis Krebs

https://www.timeshighereducation.com/sites/default/files/styles/the_breaking_news_image_style/public/bees_teamwork.jpg


Centrality Metrics: Closeness


Able to reach all the other nodes in the fewest steps


Using Metrics: Finding Key Opinion Leaders

57Source: Maven7



58Source: Maven7



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Dunbar’s number: 150

• Strong ties:

– Close, frequent

– Reciprocal

– May be embedded in a strong “local network”

• Weak ties

– Infrequent interaction

– Likely embedded in other (diverse) networks

– Accessible as needed

Source: Maven7


Centrality Metrics: Brokerage, Closure


Working cross-cluster or within clusters?


Centrality Metric: Eigenvector

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Connected to well-connected nodes


Putting Some Metrics Together

62

http://qz.com/650796/mathematicians-mapped-out-every-game-of-thrones-relationship-to-find-the-main-character/


Which Technology Scout is Most Successful?

63

It's Whom You Know Not What You Know: A Social Network Analysis Approach to Talent Management, Eoin Whelan, SSRN: http://ssrn.com/abstract=1694453

Technology ScoutConnectorGatekeeperGroup member


Using Metrics: Ego Networks and Diversity

• Organization

• Expertise

• Age, Tenure

65

External/Internal Ratio: Proportion of an individual’s ties that are in the same demographic cohort as the individual node (“ego”). Ranges from +1 (all external) to -1 (all internal)

AB’s E/I index: .308

DC’s E/I index: -.714Can be derived from any demographic:

• Social Ties

• Geographic location

• Hierarchical position


The Importance of Diversity

People who live in the intersection of social worlds are at higher risk of having good ideas. – Ron Burt

66


Organizational Networks Summary

67

• The science of networks has brought insights into the structure of organizational networks

• Organizational network analysis lets us map relationships to:

• Identify patterns of connection, disconnection, and flowsof knowledge and ideas

• Understand the roles that individuals play and their potential for enhancing organizational effectiveness

• Developing and sharing maps and metrics helps organizations to ask good questions and design targeted interventions

• A map represents a moment in time; when maps are shared the relationships start to shift


Interventions: Net Work

Ways to change patterns in networks Practices from the KM/OD Repertoire

Create more connections Make introductions through meetings and webinars, face-to-face events (like knowledge fairs); implement social software or social network referral software; social network stimulation

Increase the flow of knowledge Establish collaborative workspaces, install instant messaging systems, make existing knowledge bases more accessible and usable

Discover connections Implement expertise location and/or; discovery systems; social software; social networking applications

Decentralize Social software; blogs, wikis; shift knowledge to the edge

Connect disconnected clusters Establish knowledge brokering roles; expand communication channels

Create more trusted relationships Assign people to work on projects together

Alter the behavior of individual nodes Create awareness of the impact of an individual’s place in a network; educate employees on personal knowledge networking

Increase diversity Add nodes; connect and create networks; encourage people to bring knowledge in from their networks in the world

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Mapping Networks: Tools

http://quilting.about.com/od/picturesofquilts/ig/Alzheimer-s-Quilts/The-Ties-that-Bind.htm


What Sorts of Tools Are There?

Category of Tool What you need to know

Expert/Researcher Mappingand Analysis Tools

Range in complexity of function and cost

Emerging Platforms Network diagrams can be shared on the web

Consulting Vendors Specialized solutions with project life cycle management

Mapping social metadata Email and log file analysis

Personal networkassessment

DIY or $$$


Expert/Research Tools

…plus many more


Data Flow

Analysis

& Mapping

ToolsMaps

Metrics

Edge Data

UCINET

NetDraw

InFlow

NodeXL

Collection

Tools

Spreadsheets

Online Surveys

Paper

Node Data

Social Media


ONASurveys

• Specifically designed for doing network analysis

• Demographic questions as well as network relationship questions

• Users respond to network questions only about people they indicate they know

• Outputs datasets for:

– NetDraw/UCINET

– NodeXL

– Gephi

74


Tool Basics – the Dataset (0s and 1s)

75

Information about the nodes (vertices) and the ties (edges)


Node Attributes

76


Edges: Columns for Advice and Support

77


Open it Up …

78

What Attributes do We Want to use for the display?


Option …

79

Upload specific data when you create the NodeXL file


Size

80


Color

81


Short List of Resources for SNA/ONA Tools

82

http://tinyurl.com/SNA-ONA-Tools


Emerging Platforms: Kumu

83

https://www.kumu.io/explore


https://kumu.io/UnLtdUSA/austin-social-entrepreneurship

Kumu is Based on Community

84


Emerging Platforms: Polinode

• Create and manage surveys

• Upload and manage networks

85

https://polinode.com/


Quick Comparison

Feature/Capability Kumu Polinode

Create and manage surveys No Yes; cost is based on # of survey respondents and # of names listed

Metrics Yes Yes

Control of colors, shapes, sizes & overall diagram

GUI and CSS Stylesheets

Via GUI and specializing attributes

Publish maps on the web Yes Yes

Share data and mapping Yes Yes

Public network pricing Free • Free with basic metrics, up to 250 nodes and 1,000 edges

• $20/month for advanced metrics and up to 50,000 nodes

Private network pricing (per month)

$23 (3 projects)$34 (5 projects)$49 (10 projects)

$29

User community Yes Yes86


Network Insights Don’t Require Fancy Software

• If it’s a network, you can draw it.

87


Mapping from Social Media

• Social network platforms:

– A Facebook Friend

– A LinkedIn Connection

– A Twitter Following

• Social media content platforms:

– Likes, posts, replies, shares, and uploads

– Mentions or “retweet” #hashtags

• In-house:

– Email

88


Twitter Networks in NodeXL: Patterns

89

Polarized Crowd Tight Crowd Brand Clusters

Community Clusters Broadcast Networks Support Network

http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/


Networks in Social Media

1. Krugman tweets a link to an article

2. There are a number of Tweeters who publish links to the article but these are not connected to other Tweeters

3. There are two densely interconnected groups of people who share the link and discuss it

90

Analyzing Twitter networks with NodeXL: Broadcast Networks

http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/


Facebook from NodeXL

91


Swoop Analytics

• Use interaction data to create and analyze edges in the network

• External/internal ratios

• Edges & reciprocal edges

92

Personal and Enterprise-level dashboards


SWOOP User Characterization

• Using the metrics showing give/receive balance, SWOOP can provide feedback on typical user communication personas

• Using overall metadata, SWOOP can provide benchmark information on an organization’s online collaboration engagement/ adoption

93

http://www.swoopanalytics.com/index.php/benchmarking/

CONSULTING VENDORS

94


Consulting Vendor Options

Vendor If you are looking for… Working with Them

Maven7 OrgMapper

Complete project management of large scale (10,000’s employees) analysis for Change Management or Organizational Performance initiatives

Licensing is per survey, based on # of participants and whether or not you are certified and doing the project with them in consultation.

Syndio Social Change ManagementTalent ManagementCommunications Impact

Be their “customers for life” – bring in the tool, develop expertise and use it throughout the enterprise to manage large-scale change.

DNA-7 Organizational DesignTalent ManagementLeadership and Collaboration

Projects are one-off at this point.

Keynetiq A tool that provides 12 different survey templates, analytics, and interactive network maps with members’ profiles that employees can navigate and use to search for expertise.

Monthly fee based on number of people in the company. Custom pricing for networks with more than 1000 employees. Also available ONA consulting, study design and coordination, and full ONA project management.


Maven7 OrgMapper

• Methodology embedded in the analysis and mapping tools

– Change management (Influence)

– Organizational performance (Excellence)

• Customizations managed through the consulting services

96

Customized surveys and reports


Syndio Social

97

Syndio Social Uses SNA to Build Management Dashboards

97

Highest social capital

Most favorable to change


Keynetiq – Create a Survey

98


What to Consider in Selecting Tools

• How often will you do this in-house?

– If you want this to be an organizational competency, then you will want to designate one or more people to learn to use the tools

– If you designate someone, will it be a data junkie (who will want the DIY tools) or an organizational expert with solid computer expertise?

– If you want to do this on an occasional basis, then a consultant may be the right choice

• How much flexibility do you need?

– Do you want to run a range of metrics and dig into the data yourself or are you comfortable with using a standard set of metrics provided by a vendor?

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Summary

100

• Social network analysis tools and methods are available to map organizational as well as your individual, personal network

• The tools matter less than the network mindset – and the understanding that the structure of a network matters


http://about.me/pattianklam

• 30 years in software engineering

• 10 years in professional services knowledge management & methodology (Digital, Compaq, Nortel)

• Independent consultant 14 years; thought leader in knowledge management and social network analysis

• Charter member of Change Agents Worldwide

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