Approachable Network AnalysisJeff Horon

Gartner’s Hype Cycle

Source: http://en.wikipedia.org/wiki/File:Gartner_Hype_Cycle.svg

My Mission – Short Circuit the Hype Cycle

Source: http://en.wikipedia.org/wiki/File:Gartner_Hype_Cycle.svg

You will leave here with the knowledge skillsYou will leave here with the knowledge, skills, resources, motivation,

and ideas you need to

d t k l ido network analysis todaytoday

with data you probablywith data you probably

already have

[Social] Network Analysis

So like Facebook? Sort ofSo, like Facebook? Sort of.

B t t k hBut networks are everywhere.

And they aren’t necessarily “social.”

TopicsTopics

Networks 101Networks 101Your Use CasesT f i Y D tTransforming Your DataFree, User-Friendly SoftwareExamplesQ&AQ&A

Networks 101Networks 101

Building BlocksBuilding BlocksPutting the Pieces Together – VisualizationM t iMetrics

Building Blocks

Nodes [Vertices] – People Things IdeasNodes [Vertices] People, Things, Ideas

Links [Edges] – Relationships

or

Visualization

Metrics – Degree

HighestHighestDegree

Metrics – Degree – In-Degree

HighestgIn-Degree“Popular”Popular

Metrics – Degree – Out-DegreeHighest Out-Degree“Gregarious”g

Metrics – Betweenness

HighestHighestBetweenness“Bridge”“Commonalities”

Metrics – Betweenness

Metrics – Closeness

HighestHighestCloseness“Who could spread a rumor?”

Metrics – Closeness

Metrics – Eigenvector Centrality

HighestHighestEigenvector CentralityCentrality“Importance”

Metrics – Eigenvector Centrality

RecapD ( di d) N b fDegree (undirected): Number of

connections

In- / Out-Degree (directed): “Popular” / “G i ”“Gregarious”

Betweenness: “Bridges” / “Commonalities”

Closeness: “Rumor starting point”

Eigenvector Centrality: “Importance”

Your Use Cases – Connect:

People to Other PeoplePeople to Other People

Things/Ideas to Other Things/Ideas

People to Things/Ideas

If the other attendees are starting to look like this to you…like this to you…

Transforming Your DataTransforming Your Data

Common Network Data StructuresCommon Network Data StructuresRelational DatabaseU t t d T tUnstructured Text

Edge List

A list of edges (links)!

A BA CB CB C

Edge List

A list of edges (links)!

A B A BA C A CB C B CB C B C

Edge List

A list of edges (links)!

A B A BA C A CB C B CB C B C

AA

B C

Data You May Already HaveData You May Already Have

Faculty/Staff and Appointing DepartmentsFaculty/Staff and Appointing DepartmentsFaculty/Staff and GroupsP i i l I ti t d S dPrincipal Investigators and Sponsored

ProjectsSponsored Projects and ParticipantsAuthors and Publications

Adjacency Matrix

A table of each node by each node

A B C DA| x 1 1 0 AB| 1 x 1 0B| 1 x 1 0C| 1 1 x 0 B CD| 0 0 0D| 0 0 0 x

D

Transforming Relational Database Data

Where your data has unique identifiers and features associated with them such as:features associated with them, such as:

Transforming Relational Database Data

Join two instances of your table by the unique identifier:unique identifier:

Transforming Relational Database Data

Query for both instances of the feature, returning:returning:

Transforming Relational Database DataNetwork analysis software will remove “self-loops”

and duplicate edges:g

Transforming Relational Database Data

And the resulting visualization might look like:like:

Unstructured Text

Node: Word or phraseLink: Co-occurrence within a block of textLink: Co-occurrence within a block of text

Suppose we wanted to find co occurrencesSuppose we wanted to find co-occurrences among words in unstructured text and words of interest included “network” andwords of interest included network and “text.”

You can construct a network based upon word co-occurrence in unstructured textword co-occurrence in unstructured text.

Unstructured TextYou can construct a network based upon

word co-occurrence in unstructured text.

Edge ListEdge List

network texttext network

Free, User-Friendly Software

NodeXL [http://nodexl.codeplex.com/][ p p ]

-Microsoft Research / University CollaboratorsMicrosoft Research / University Collaborators

-Installs as an Excel 2007 Template-Installs as an Excel 2007 Template

Free easy and powerful with top notch-Free, easy, and powerful with top-notch visualization

Free, User-Friendly Software

Simple Text/Network Mining p g

-Homegrown Excel/Visual Basic Package-Homegrown Excel/Visual Basic Package

-Tech Transfer [http://techfinder.techtransfer.umich.edu/ -Search for # 4730]

LiveLiveDemoDemo

Specific Examples

Things/Ideas and Other Things/Ideas

Concepts and Other Concepts inConcepts and Other Concepts in Publications and Sponsored Project Proposal / Award DataProposal / Award Data

Concepts and Other Concepts in Publications and Sponsored Project Proposal / Award Data

C tConceptIncreasing Betweenness Centrality

Specific ExamplesSpecific Examples

People and Things/IdeasPeople and Things/Ideas

People and Sponsored Projects

Authors and Publication ConceptsAuthors and Publication Concepts

People and Sponsored ProjectsMedical School PI Engineering PIMedical School Project Engineering Project

Specific ExamplesSpecific Examples

People and Other PeoplePeople and Other People

Co-Participation on Sponsored Projects,Co-Authorship

Co-Participation on Sponsored Projects, Co-AuthorshipResearcher / Author Active Project + PublicationI i Ei t C t lit A ti P j tIncreasing Eigenvector Centrality Active Project

Publication

Strategies for CommunicationStrategies for Communication

VisualizationVisualization-Pay attention to node layoutS btl d h d t-Subtly encode as much data as you can

-Include a really simple key

You understand the network dataYou understand the network data, visualization, and metrics + your audience doesn’t = hand deliverdoesn t hand deliver

Q&A

ResourcesResourceshttp://nodexl.codeplex.com/ p p

http://www.umich.edu/~jhoron

Tech Transfer # 4730

On Campus: School of Information, Center for Positive Organizational ScholarshipPositive Organizational Scholarship, Interdisciplinary Group for Research on Innovation