Identifying Collaborative Relationships and Interconnections Between Research Communities Using LinkedIn Maps David Eichmann University of Iowa Noshir.

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Presentation transcript:

Identifying Collaborative Relationships and Interconnections Between Research Communities Using LinkedIn Maps David Eichmann University of Iowa Noshir Contractor, Northwestern University Holly J. Falk-Krzesinski, Elsevier & Northwestern University Melissa Haendel, Oregon Health Science University Michael Conlon, University of Florida

Current Approaches to Team Identification Survey the target community Suffers from issues of scale and detection Quantitatively analyze a surrogate information source Publication/Grant co-authorship Temporally offset from actual collaboration Only the ‘winners’ are detected Serious information loss re true expertise

Some Early Data on CTSA Consortium Collaboration Inter-Institutional co-authorship pair counts Org. Cornell NW OHSU UCSF Fla Iowa 66 1944 258 25 4 40 267 52 353 36

The Path to Now Holly comes across the new service out of LinkedIn Labs, visualization of your LinkedIn connections http://inmaps.linkedinlabs.com Holly relates this coolness to Dave, who can’t resist poking about to see if he can scrape the data Having done so, he twists arms of selected colleagues to cough up their maps

Dave’s Map

Mike’s Map

Melissa’s Map

Holly’s Map

Nosh’s Map

Phase 1 Acquisition of graph structure Nodes, edges, coordinates, cluster membership Acquisition of node characteristics Person name, URL, public ID

Aggregate Graph Statistics Person # Nodes # Edges Ave. Edges/Node Dave 339 2,816 8.3 Holly 1,835 13,930 7.6 Melissa 272 2,577 9.5 Michael 461 5,571 12.1 Noshir 2,373 27,203 11.5

Subgraph Size Subgraph Dave Holly Melissa Mike Nosh 96 127 45 189 1 37 96 127 45 189 1 37 124 41 103 155 2 36 123 40 69 217 3 34 33 72 212 4 25 108 26 81 143 5 23 105 24 247 6 18 102 15 144

Overall Population Characteristics Total distinct individuals: 4959 Shared by 2 or more: 246 Shared by 3 or more: 43 Shared by 4 or more: 22 Shared by 5 or more: 10

For the 5 or more The primary participants (except Nosh!) Others: Bill Barnett Ying Ding Kristi Holmes Warren Kibbe Titus Schleyer Griffin Weber

Phase 2 Screen scrape the public page for a person # of connections (capped at 500) Organizational affiliations Expertise endorsements

Subgraph Intersection (Dave) Holly Melissa Mike Nosh 2 8 24 3 1 - 6 14 25 4 5

Subgraph Expertise Characterization Cluster 0

Subgraph Expertise Characterization Cluster 2

Subgraph Expertise Characterization Cluster 3

Pattuelli’s Spectrum of Relationships (2012)

Pattuelli’s Spectrum of Relationships (2012) RN Tools

Pattuelli’s Spectrum of Relationships (2012) Linked In RN Tools

Pattuelli’s Spectrum of Relationships (2012) Ontologies used foaf (Friend of a Friend) rel (Relationship) mo (Music) Echos of Trigg’s link taxonomy Trigg, R. 1983. Network-Based Approach to Text Handling for the Online Scientific Community. Ph.D. dissertation, Department of Computer Science, University of Maryland, technical report TR-1346

Observations N = 5 ! LinkedIn expertise endorsements are an ad hoc folksonomy Melding this with the typically controlled vocabulary of the research networking tools should prove interesting These characteristics don’t show up in the RN meta-data

Questions? Email: david-eichmann@uiowa.edu Thanks to my co-authors and the Research Networking Affinity Group Supported in part by NIH grants 2 UL1 TR000442-06 and UL1 RR024979