The first day of Sunbelt is finished: it was very hot … meaning there were some problems with conditioning air not working ;)
I met some cool people: in particular
(1) Mathieu Bastian of Gephi, great open source program for visualization of networks,
(2) Jure Leskovec of Stanford, hands-down best talk up to now, who spoke about “Predicting Positive and Negative Links in Online Social Networks”, work on Wikipedia, Slashdot and Epinions signed social networks (they even cited me in the paper and used the Epinions trust network I made available time ago on Trustlet.org!),
(3) Filippo Menczer of Indiana University, whose great Scholarometer widget I recently embedded on my blog and who is doing many different great works.
Some people are using the hashtag #sunbelt on Twitter, you might enjoy posts tagged as #sunbelt as made visible by visibletweets (iframed below)
Last point, I’m at Sunbelt with my colleagues in the SoNet group, Michela Ferron and Asta Zelenkauskaite. Tomorrow we will present two recent works: one about
social networks in Wikipedia, the other about social capital and enterprise2.0 platform usage.
Now back to finish the slides …
The goal of this project is to integrate financial capital, human capital and social network analysis to formally and quantitively define and model social capital. Our interest is to find out what are the real dollar value of social networks.
Nine hundred and forty-eight dollars.
That’s the annual dollar value of each person in your email address book at work, according to a novel IBM study published in the Winter Information Systems Conference in February 2009.
IBM researchers, together with researchers in MIT, were looking to scientifically determine how valuable electronic social networks are, such as those in a group that primarily communicates electronically. Using mathematical formulas, honed by observing the email traffic and financial success of 2,600 anonymized far flung IBM consultants collaborating on thousands of projects during one year, researchers found that not all email relationships were equal.
In fact, they found that people with strong email ties with a manager, or had a more diverse circle of correspondents, enjoyed greater financial success than those who were more aloof. Teams with an even mix of genders also performed well financially. Individuals have more diverse networks and thus have more people who are reachable within 2 social steps (i.e., your friends’ friends’ friends.) is valuable. Too intensive communications to the same people have negative impact, perhaps because of the repetitive redundant information exchange.
From the “social network” page on Wikipedia:
Some researchers have suggested that human social networks may have a genetic basis. Using a sample of twins from the National Longitudinal Study of Adolescent Health, they found that in-degree (the number of times a person is named as a friend), transitivity (the probability that two friends are friends with one another), and betweenness centrality (the number of paths in the network that pass through a given person) are all significantly heritable. Existing models of network formation cannot account for this intrinsic node variation, so the researchers propose an alternative “Attract and Introduce” model that can explain heritability and many other features of human social networks.
 # ^ “Genes and the Friends You Make”. Wall Street Journal. January 27, 2009. http://online.wsj.com/article/SB123302040874118079.html.
 # ^ Fowler, J. H. (10 February 2009). “Model of Genetic Variation in Human Social Networks” (PDF). Proceedings of the National Academy of Sciences 106 (6): 1720–1724. doi:10.1073/pnas.0806746106. http://jhfowler.ucsd.edu/genes_and_social_networks.pdf
Some papers are more worth than others.
Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study by James H Fowler and Nicholas A Christakis.
Solid analysis based on data from 4739 individuals followed from 1983 to 2003.
Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.
Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.
Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends).
People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future.
Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation.
(credits: Photo by beija-flor released on Flickr under Creative Commons Attribution Noncommercial No Derivative license)