Social Networks in the News at NYT

My colleague Scott Sargent at Telligent notes that there are two sections of the March 29th Sunday New York Times feature articles illustrated with network graphs.  The Business section runs an article “Is Facebook Growing Up Too Fast? ( and the Style Section has an article on The Celebrity Twitter Ecosystem.

20090329 NYT Facebook Ego Networks

20090329 NYT Facebook Ego Networks

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: in particular 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 ( 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.

2009 -03- 29 - NYT - Twitter Ecosystem

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:

Deanonymizing Social Networks – Arvind Narayanan & Vitaly Shmatikov

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

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.

The Future of Helath Insurance: Mobile Medical Sensors and Dynamic Pricing

Photo Credit: Elizabeth Churchill

After a lovely dinner you order a third glass of wine, take a sip and your mobile phone rings.

Its your health insurance company’s computer. You take the call.

“We’re delighted to see you are enjoying yourself this evening!” the cheerful voice synthesis exclaims.

“Please note, however, that you are now at the policy limit for blood alcohol level. To purchase a 24 hour waiver that will allow you to exceed this limit, press 1. A fee of $20 will be charged to your monthly co-payment invoice. To decline waiver coverage, press 2. Please note that if you decline coverage your blood alcohol level must drop under the policy limit within 45 minutes. Continued elevated blood alcohol above your policy limit will constitute breach of your coverage agreement and you may loose all coverage for alcohol related illnesses including injuries received from accidents that may be caused by impairment.”

With a paid-for glass of wine on the table, many people may just go along with the offer (their judgment may already be somewhat impaired).

Real-time health monitoring is an application that is rapidly developing.  Our mobile devices are likely to play a central role in enabling a wide scale real-time continuous monitoring of health related data.  The Quantified Self meet-up is exploring many aspects of this space, but discussion of ways large institutions could use this data has not been a large focus so far.

Medical compliance is a huge issue: most of us do not take prescribed medicines the way we are “supposed to” – skipping doses, doubling up, or quitting a series of medications once symptoms subside.  A real-time assessment of our blood (among other measures) would go a long way to reducing the costs of illness and treatment.  In a world of scarce medical resources, can competitive societies allow their members to be slothful and subsequentially expensive to maintain?

The Intel Health Guide is a good example of this emerging trend, although still in a “desktop” form factor.

Intel Health Guide Device

Other examples can be found in the iPhones app store where health and navigation and travel categories of applications are starting to collide.