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Approachable Network Analysis
 

Approachable Network Analysis

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Unlock the power of network structures in your data. Learn how to build and analyze networks to gain insights through relationship analysis. Apply approachable techniques and free, user-friendly ...

Unlock the power of network structures in your data. Learn how to build and analyze networks to gain insights through relationship analysis. Apply approachable techniques and free, user-friendly software. Transform the data you have into the data you need – from relational databases and unstructured text to common network structures.

Jeff detailed his work in the Medical School Grant Review & Analysis Office. Examples will include: Identifying networks of collaborators from eResearch Proposal Management [eRPM PAF] data, discovering networks of concepts in unstructured text, and use cases from other administrative data sets. Jeff’s presentation included:

-“Networks 101″ – The basic building blocks of networks
-How people in any business unit can apply network analysis
-An emphasis on approachable techniques and free, user-friendly software
-Strategies for effectively visualizing and sharing network-driven insights
-Tools, tips, and tricks

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    Approachable Network Analysis Approachable Network Analysis Presentation Transcript

    • Approachable Network Analysis Jeff 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, skills skills, resources, motivation, and ideas you need to do t d network analysis k l i today with data you probably already have
    • [Social] Network AnalysisSo,So like Facebook? Sort of of.But tB t networks are everywhere. k hAnd they aren’t necessarily “social.”
    • TopicsNetworks 101Your Use CasesTransforming YT f i Your D tDataFree, User-Friendly SoftwareExamplesQ&A
    • Networks 101Building BlocksPutting the Pieces Together – VisualizationMetricsM ti
    • Building BlocksNodes [Vertices] – People Things Ideas People, Things,Links [Edges] – Relationships or
    • Visualization
    • Metrics – Degree Highest Degree
    • Metrics – Degree – In-Degree Highest g In-Degree “Popular” Popular
    • Metrics – Degree – Out-Degree Highest Out-Degree g “Gregarious”
    • Metrics – Betweenness Highest Betweenness “Bridge” “Commonalities”
    • Metrics – Betweenness
    • Metrics – Closeness Highest Closeness “Who could spread a rumor?”
    • Metrics – Closeness
    • Metrics – Eigenvector Centrality Highest Eigenvector Centrality “Importance”
    • Metrics – Eigenvector Centrality
    • Recap Degree ( di D (undirected): N b of d) Number f connections In- / Out-Degree (directed): “Popular” / “Gregarious” “G i ” Betweenness: “Bridges” / “Commonalities” Closeness: “Rumor starting point” Eigenvector Centrality: “Importance”
    • Your Use Cases – Connect:People to Other PeopleThings/Ideas to Other Things/IdeasPeople to Things/Ideas
    • If the other attendees are starting to looklike this to you…
    • Transforming Your DataCommon Network Data StructuresRelational DatabaseUnstructured TextU t t dT t
    • Edge ListA list of edges (links)!A BA CB C
    • Edge ListA list of edges (links)!A B A BA C A CB C B C
    • Edge ListA list of edges (links)!A B A BA C A CB C B C A B C
    • Data You May Already HaveFaculty/Staff and Appointing DepartmentsFaculty/Staff and GroupsPrincipal Investigators and SPi i lI ti t d Sponsored d ProjectsSponsored Projects and ParticipantsAuthors and Publications
    • Adjacency MatrixA table of each node by each node A B C DA| x 1 1 0 AB| 1 x 1 0C| 1 1 x 0 B CD| 0 0 0 x D
    • Transforming Relational Database DataWhere your data has unique identifiers and features associated with them such as: them,
    • Transforming Relational Database DataJoin two instances of your table by the unique identifier:
    • Transforming Relational Database DataQuery for both instances of the feature, returning:
    • Transforming Relational Database DataNetwork analysis software will remove “self-loops” and duplicate edges: g
    • Transforming Relational Database DataAnd the resulting visualization might look like:
    • Unstructured TextNode: Word or phraseLink: Co-occurrence within a block of textSuppose we wanted to find co occurrences co-occurrences among words in unstructured text and words of interest included “network” and network “text.”You can construct a network based upon word co-occurrence in unstructured text text.
    • Unstructured TextYou can construct a network based upon word co-occurrence in unstructured text.Edge Listnetwork texttext network
    • Free, User-Friendly SoftwareNodeXL [http://nodexl.codeplex.com/] [ p p ]-Microsoft Research / University Collaborators Microsoft-Installs as an Excel 2007 Template-Free, easy, and powerful with top notch Free easy top-notch visualization
    • Free, User-Friendly SoftwareSimple Text/Network Mining p g-Homegrown Excel/Visual Basic Package-Tech Transfer [http://techfinder.techtransfer.umich.edu/ - Search for # 4730]
    • LiveDemo
    • Specific ExamplesThings/Ideas and Other Things/IdeasConcepts and Other Concepts in Publications and Sponsored Project Proposal / Award Data
    • Concepts and Other Concepts in Publications and Sponsored Project Proposal / Award Data Concept C t Increasing Betweenness Centrality
    • Specific ExamplesPeople and Things/IdeasPeople and Sponsored ProjectsAuthors and Publication Concepts
    • People and Sponsored Projects Medical School PI Engineering PI Medical School Project Engineering Project
    • Specific ExamplesPeople and Other PeopleCo-Participation on Sponsored Projects, Co-Authorship
    • Co-Participation on Sponsored Projects, Co-Authorship Researcher / Author Active Project + Publication Increasing Ei I i Eigenvector C t lit t Centrality Active Project A ti P j t Publication
    • Strategies for CommunicationVisualization -Pay attention to node layout -Subtly encode as much d t as you can S btl d h data -Include a really simple keyYou understand the network data data, visualization, and metrics + your audience doesn t doesn’t = hand deliver
    • Q&A
    • Resourceshttp://nodexl.codeplex.com/ p phttp://www.umich.edu/~jhoronTech Transfer # 4730On Campus: School of Information, Center for Positive Organizational Scholarship Scholarship, Interdisciplinary Group for Research on Innovation