Complexity and Social Networks Blog: September 2009 Archives

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22 September 2009

Sorry for my absence .....

I'm back, and promise to keep you entertained and informed.
I will also promise to cut the incessant barking!

(Of course, it's not really MY blog .... I am only a tiny little part. And I thank David for his patience.)

Posted by Stan Wasserman at 6:39 PM | Comments (3)

Future Networks Conference at MIT

For those of you in the Boston area, there will be a one day conference at MIT on Future Networks - Economy, Energy, Health. A lot of the local networks researchers are talking here, particularly those with more of a computer science bent.

Ben Shneidernman from the University of Maryland is giving the keynote, and friends of the blog Marta Gonzalez and Cesar Hidalgo will also be speaking (along with myself). Hope to see many of you there.

Posted by Ben Waber at 2:32 PM | Comments (0)

21 September 2009

Communities in Networks

Uncovering the "community" structure of social networks has a long history, but communities play a pivotal role in almost all networks across disciplines. Intuitively, one can think of a network community as consisting of a group of nodes that are relatively densely connected to each other but sparsely connected to other dense groups of nodes. Communities are important because they are thought to have a strong bearing on functional units in many networks. So, for example, communities in social networks can correspond to different social groups, such as family, whereas web pages dealing with a given subject tend to form topical communities.

The concept is simple enough, but it turns out that coming up with precise mathematical definitions and algorithms for community detection is one of the most challenging problems in network science. Recently, a lot of the research in this area has been done using ideas from statistical physics, which has an arsenal of tools and concepts to tackle the problem. Unfortunately (but understandably) relatively few non-physicists like to read statistical physics papers.

Together with my colleagues Mason Porter (Oxford University) and Peter Mucha (University of North Carolina at Chapel Hill), we thought it would be useful to let others take a peek at some of this work. In an effort to put in context some of the hundreds of papers, we recently compiled an introductory review on some of our favorite approaches to community detection. While there are excellent existing reviews, our "Communities in Networks", published by Notices of the American Mathematical Society (AMS), tries to make sense of this smorgasbord of methods and, hopefully, lets a broader audience get a flavor of this exciting field.

I hope to be making a couple of postings on community structure and community detection later on. In the meantime, you can see for yourself if we have succeeded by checking out the freely accessible article on the AMS website, or by going to arXiv or SSRN.

The largest connected component of a coauthorship network connecting physicists who have published together on networks. Each node is colored according to community membership.

Posted by JP Onnela at 9:40 AM | Comments (2)

20 September 2009

Upcoming "Workshop on Information in Networks"

While I am making announcements, I will also mention the upcoming Workshop on Information in Networks, taking place Sept 25-26 in NYC. This workshop, put together by Sinan Aral at NYU, pulls together a multidisciplinary all star cast of scholars in this area. A brief description from the website:

WIN is a Social Networks Summit intended to foster collaboration and to build community. The increasing availability of massive networked data is revolutionizing the scientific study of a variety of phenomena in fields as diverse as Computer Science, Economics, Physics and Sociology. Yet, while many important advances have taken place in these different communities, the dialog between researchers across disciplines is only beginning. The purpose of WIN is to bring together leading researchers studying 'information in networks' - its distribution, its diffusion, its value, and its influence on social and economic outcomes - in order to lay the foundation for ongoing relationships and to build a lasting multidisciplinary research community.

Posted by David Lazer at 11:32 AM | Comments (0)

Welcoming a new blogger to the team: Jukka-Pekka Onnela

I am pleased to introduce a new blogger to our team: Jukka-Pekka Onnela. Jukka-Pekka has been a Fulbright-funded postdoc with me for the last year, and holds a PhD from the Helsinki University of Technology, Finland, in Complex Systems. He was lead author on a paper in PNAS on which I was a coauthor, "Structure and tie strengths in mobile communication networks," and has done a wide array of thought provoking research on various (usually human) complex systems. It is a pleasure to welcome him to the netgov blog.

Posted by David Lazer at 11:28 AM | Comments (1)

10 September 2009

Learning from Chemical Traces

A few weeks ago a really fantastic study got a lot of press about how researchers found that 90% of US bills have trace amounts of cocaine on them. This got me thinking about some of the other interesting currency studies that have been done.

Where is George? comes to mind as another brilliantly designed study. Researchers stamped thousands of bills with a URL where people who received the bill could go and enter its current location. The researchers got a huge number of responses, allowing them to use bill mobility patterns to approximate human mobility patterns. Of course with the recent availability of high quality cell phone and sensor data this may not be the best data collection method in the future, but at the very least it's a great study design.

But the cocaine study got me thinking: what else can we learn about people's habits from chemical traces on bills? Of course the reason cocaine can be detected is that it binds to the green dye in money, but a large number of other compounds would likely also bind to this dye. Can you learn about fast food consumption from bill traces? Could you gauge the "stress level" of the country by measuring the amount of certain sweat compounds?

You can potentially get this data from other sources, but often it's hard to get a large enough cross section of society to get a broad enough picture. By combining analysis of these physical traces with digital traces, we can get closer to having a complete view of how our society is behaving.

Posted by Ben Waber at 3:17 PM

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