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Network Science at #ENYACRL2015
- 1. Network Science 6 Degreesof Kevin Bacon for Librarians #ENYACRL2015
- 2. The Problem withKevin Bacon
- 3. RANDOM NETWORKS? How doesorder come from disorder?
- 4. Networks have properties –Follow rules=predictable
- 6. West Nile Virus GeneticsScientist Collaboration Network
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- 10. Yeah but…Librarians? Mission Statement Ourmission is to connect people and the resources they need we must understand how information and human resources flow. What better view than to see the whole picture through the fundamentals of the network?
- 11. DIY • Social Networks •TAGS + Google Fusion • Resource Networks • Open data sets (local demographics) • Circulation data
- 12. • Harvest usingTAGS: https://tags.hawksey.info/get-tags/
- 13. Google Fusion • Saveas csv • Upload matrix, cards, graph
- 14. • Visualize withGoogle Fusion tables • https://www.google.com/fusiontables/DataSource?docid =1842W_7Vame71CRxIBc6_TEmbub2l1TqPPp61368S • Publish (http://bit.ly/1ICEqtz) #CILDC
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Editor's Notes
- #2 MLIS ‘14 grad | CAS in data science. Working on NSF grant doing network analysis with Dr. Jian Qin on genetics R&D scientists collaboration Who writes papers together? (pub network) Who submits DNA sequences together? (sub network) Tweet out from these slides using plugin? Scrape all the tweets associated with dayofFITS and librarybacon. (or librarynetsci).
- #3 We don’t know the nth degree! We are not aware of the shortest path to our target. It’s like being lost in a maze where we can only see the hallways and doors. But if we had a map of the maze, we’d be out in 5 minutes. but we CAN! Linkedin Twitter. The concept of social distance is easily understood by considering the common parlor and roadtrip game, six degrees of Kevin Bacon, in which players try to find the shortest set of connections from any actor to Kevin Bacon based on movies that actors have starred in together.
- #4 Philosophical question. We’re just a bunch of people milling around. But the universe displays predictable behavior at the large scale, even humans. Just like water ice, we are in a state of randomness that turns into phase change. Are real networks in a constant state of order to disorder? Why do hubs appear in networks of all kinds, ranging from actors (Kevin Bacon) to the Web? Why are they al described by power laws? Are there fundamental laws forcing different networks to take up the same universal form and shape? How does nature spin it’s webs? “How nature works is a question of equal interest to scientists, philosophers and librarian alike” True, it’s hard to predict at indivudal level. But we are very predictable at the aggregate level. “Though randomness is involved in every diffusion process, each process follows laws that can be formulated in precise mathematical terms.” p 141. Systems theory, and chaos theory. Consciousness arising from WWW systems?
- #5 Property then implication (example). Amazing degree of mathematical consistency”
- #7 In collaborative science research Scientists submit sequences to GenBank. We extract metadata (name, how many people they have co-authored or sequenced with, country, etc.) R: iGraph package And voila! We see some are super stars, some just submit once, Nobel Prize dude (The Kevin Bacon celeb of Genetics R&D)
- #8 How do we measure the big guns? Measuring the big fish in the pond http://cs.brynmawr.edu/Courses/cs380/spring2013/section02/slides/05_Centrality.pdf http://www.activatenetworks.net/who-is-central-to-a-social-network-it-depends-on-your-centrality-measure/
- #9 Individuals who are highly connected to others within their own cluster will have a high closeness centrality. Applications: High closeness centrality individuals tend to be important influencers within their local network community. They may often not be public figures to the entire network of a corporation or profession, but they are often respected locally and they occupy short paths for information spread within their network community.
- #10 p. 41. The importance of weak social ties in our lives. From spreading rumors to getting a job the importance of weak ties. Those who act as bridges between clusters in the network have high betweenness centrality. Applications: High betweenness individuals are often critical to collaboration across departments and to maintaining the spread of a new product through an entire network. Because of their locations between network communities, they are natural brokers of information and collaboration. One difference between high betweenness individuals in a network and actual brokers is the latter usually have a public profile as part of their business, whereas high betweenness individuals often are overlooked. This occurs because they are not central to any single social clique, and instead reside on the periphery of several such cliques each of which all engender more trust and admiration within rather than outside of the clique.
- #11 3 ways: social and resource networks (community understanding) Indexing (web directional maps)
- #12 We all know social networks look like this: (Facebook, Twitter, Duck faces, surprise selfies, etc.) Books, Mystery, romance, (entreprenuer). What flavors? Weeeding the collection, choosing new purchases? Programming. Marketing, AIDs network, grocery store. #ebola? But what do Resource or Information networks look like? WWW, It’s not just the hubs that matter: it’s the connectors. Think of Jill. Jill knows everyone and can connect you with Virus networks?
- #13 Any EL will do.
- #14 Any EL will do.
- #16 References: Albert Laslo Barabasi http://barabasilab.com/LinkedBook/ http://blogs.discovermagazine.com/crux/2015/03/25/infected-emotions/#.VRbsjPnF-So