The June 9 issue of New Scientist contains an article featuring a map of scientific citation that was generated by NodeXL.

The article about the patterns of citation and the time to publication for US and non-US based scientists contains a map of which papers and authors cite which other papers and authors.

From New Scientist:

Our analysis of more than 200 research papers from 2006 onwards reveals that US-based scientists are enjoying a significant advantage, getting their papers published faster and in more prominent journals (find our data, methods and analyses here). The disparity is likely to spark debate when theInternational Society for Stem Cell Research (ISSCR) meets in San Francisco next week.

Description of the methods used to generate the image is available here.

“To map the network of citations, we imported the data into a Microsoft Excel spreadsheet running a social-network analysis extension called NodeXL. Citations erroneously discarded by Sitkis were entered manually into this spreadsheet.

To reveal a core network of citations among the primary authors, we filtered to exclude all connections where the scientists cited one another’s work less than four times, and drew the diagram so that its elements reflected the number and strength of the remaining links: the more citations, the larger the arrows; the more incoming links, the larger the node representing that scientist. Finally, we adjusted the layout manually to most clearly show any differences in the network based on author location.

There were 43 scientists in the network, 24 from the US and 19 from elsewhere. All but two researchers – one in the US and one in Germany – connected to Shinya Yamanaka of Kyoto University in Japan, the pioneer of cellular reprogramming, but non-US scientists had no other links among themselves. The US side of the diagram showed a richer network of links, with 33 connections among 17 scientists. Seven of the non-US scientists, including Yamanaka, connected with US-based researchers, making a total of 16 links. However, no scientist working in the US linked to any non-US scientist other than Yamanaka.” [Link]

Prof. Jen Golbeck and Justin Grimes analyzed 6,000 tweets from United States Congress members.  They found some interaction but a dominant broadcast pattern of use with a focus on self-promotion.  The Washington Post headlines the results as “Tweeting Their Own Horns Study Finds Posts By Lawmakers Boastful or Boring”

Here is the video:

From the UMD News desk:
“A new study by University of Maryland researchers finds a
growing use of Twitter among members of Congress, but that
the purpose and content of their messages fall short of
improving government transparency.

Jennifer Golbeck, assistant professor in the College of Information
Studies, Maryland’s iSchool, a doctoral student and an undergraduate
assistant analyzed more than 5,000 tweets sent by 69 members of
Congress in February. They found that House and Senate members
were using the social media platform mostly to promote themselves,
rather than engage in dialogue with constituents and the public at large.

“Members of Congress were not sharing much new information on
Twitter, and there were few posts that improve transparency,”
Golbeck says.”

This weekend is the Social Computing 2009 conference in Vancouver, B.C.  It is a gathering of many people doing research on social media useage.  Many papers are about tagging systems, blogs, wikis, message boards, and social networking services.

Along with several co-authors, I contributed to three papers in this year’s conference:

Bonsignore, E.M., Dunne, C., Rotman, D., Smith, M., Capone, T., Hansen, D.L. & Shneiderman, B. (2009), “First steps to NetViz Nirvana: evaluating social network analysis with NodeXL”, In SIN ’09: Proc. international symposium on Social Intelligence and Networking. IEEE Computer Society Press.

Abstract: Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, integrates a library of common network metrics and graph layout algorithms within the familiar spreadsheet format, offering a potentially low-barrier to-entry framework for teaching and learning SNA. We present the preliminary findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. The majority of students, while information professionals, had little technical background or experience with SNA techniques. Six of the participants had more technical backgrounds and were chosen specifically for their experience with graph drawing and information visualization. Our primary objectives were (1) to evaluate NodeXL as an SNA tool for a broad base of users and (2) to explore methods for teaching SNA. Our complementary dual case-study format demonstrates the usability of NodeXL for a diverse set of users, and significantly, the power of a tightly integrated metrics/visualization tool to spark insight and facilitate sensemaking for students of SNA.

Abstract: Broadening adoption of social media applications within the enterprise offers a new and valuable data source for insight into the social structure of organizations. Social media applications generate networks when employees use features to create “friends” or “contact” networks, reply to messages from other users, edit the same documents as others, or mention the same or similar topics. The resulting networks can be analyzed to reveal basic insights into an organization’s structure and dynamics. The creation and analysis of sample social media network datasets is described to illustrate types of enterprise networks and considerations for their analysis.

Abstract: Community based Question and Answer systems have been promoted as web 2.0 solutions to the problem of finding expert knowledge. This promise depends on systems’ capacity to attract and sustain experts capable of offering high quality, factual answers. Content analysis of dedicated contributors’ messages in the Live QnA system found: (1) few contributors who focused on providing technical answers (2) a preponderance of attention paid to opinion and discussion, especially in non-technical threads. This paucity of experts raises an important general question: how do the social affordances of a site alter the ecology of roles found there? Using insights from recent research in online community, we generate a series of expectations about how social affordances are likely to alter the role ecology of online systems.

There is some very positive coverage of Telligent recently in the press.  First there is the very detailed review of the community platform and its reporting features from Barb Mosher at CMS Wire: “Social Web Analytics with Telligent’s Harvest Reporting Server“.  The article does a great job of detailing the features of the Harvest social media analysis product.  The article’s explanation of what social media analytics are is itself useful:

“You need to measure what’s happening in your community. If you are interested in knowing what your community members are up to, what information they are sharing and looking at, what they are saying about you, your product or your service (positive and negative), then you need social analytics.

If you need to know how many users are signing up, how many are contributing to blogs, wikis, forums, how many are asking and answering questions, then you need social analytics.

With social computing becoming much more mainstream and in many cases, a requirement for both external and internal relationship building, it has become critical to measure the impact these solutions really have. You also need to know how and where to improve these solutions.

And you aren’t going to get this information from traditional web traffic analytics.”

A mention of Telligent in the New York Times is also cause for note: the recent article on help communities listed Telligent as a provider of platforms, along with Jive, Lithium, and HelpShare, that enable companies to host communities of passionate users who help one another solve problems with their products.  There is a great quote from Natalie Petouhoff, Forrester Research analyst:

Natalie L. Petouhoff, an analyst at Forrester Research, said that online user groups conform to what she calls the 1-9-90 rule. About 1 percent of those in the community, she explained, are super-users who supply most of the best answers and commentary. An additional 9 percent are “responders” who mainly reply and rate Web posts, she said, and the other 90 percent are “readers” who primarily peruse and search the Web site for useful information.

“The 90 percent will come,” Ms. Petouhoff said, “if you have the 1 percent.”

I would extend this point and add: within the 1% of active users are all the different types of active contribution, both good and bad.  Top answer people, discussion starters, discussion people, question people, and flame warriors all crowd into this sliver of the online demographic.  It is important to have the tools to separate the different kinds of active contributions to be sure that an active community is also a properly productive one!

My colleague Prof. Ben Shniederman is positively impressed by these images.  He writes, “Notice how the node layout remains stable as edges are removed, so by the 4th figure the edges can all be followed easily….”.  This is one of the themes he highlights in his paper and presentations about problems and improvements in network graph drawing (see: http://www.cs.umd.edu/hcil/nvss/and in particular http://www.cs.umd.edu/hcil/pubs/presentations/NVSS-3.ppt). Prof. Shniederman’s  5th edition of Designing the User Interface is now available with two full chapters on the website with wordles to open each chapter.

A somewhat related article ran the same day in the Style section on The Celebrity Twitter Ecosystem (http://www.nytimes.com/2009/03/29/fashion/29twitter.html). This image focused on the linkages between well known people using Twitter and, by extension, revealing who they follow and who follows them in the social network.

In the first image no names are associated with the nodes, in the second the names are the major point of the diagram.

The practice of “anonymization” of network graphs may be moot in light of a recent publication mentioned on the Social Network Analysis email list (SOCNET) by Mark Round from QinetiQ of a paper:

De-anonymizing Social Networks – Arvind Narayanan & Vitaly Shmatikov
(http://33bits.org/2009/03/19/de-anonymizing-social-networks/)

which suggests that just publishing the unique pattern of links around an individual is sufficient to identify them in an otherwise anonymized data base.

Abstract:
Operators of online social networks are increasingly sharing
potentially sensitive information about users and their relationships
with advertisers, application developers, and data-mining researchers.
Privacy is typically protected by anonymization, i.e., removing names,
addresses, etc.
We present a framework for analyzing privacy and anonymity in social
networks and develop a new re-identification algorithm targeting
anonymized social-network graphs. To demonstrate its effectiveness on
real-world networks, we show that a third of the users who can be
verified to have accounts on both Twitter, a popular microblogging
service, and Flickr, an online photo-sharing site, can be re-identified
in the anonymous Twitter graph with only a 12% error rate.
Our de-anonymization algorithm is based
purely on the network topology, does not require creation of a large
number of dummy “sybil” nodes, is robust to noise and all existing
defenses, and works even when the overlap between the target network
and the adversary’s auxiliary information is small.