Redes afectivas en una investigación sobre un colegio secundario estadounidense

RESEARCHERS MAP THE SEXUAL NETWORK OF AN ENTIRE HIGH SCHOOL

COLUMBUS, Ohio – For the first time, sociologists have mapped the romantic and sexual relationships of an entire high school over 18 months, providing evidence that these adolescent networks may be structured differently than researchers previously thought.

James Moody

The results showed that, unlike many adult networks, there was no core group of very sexually active people at the high school. There were not many students who had many partners and who provided links to the rest of the community.

Instead, the romantic and sexual network at the school created long chains of connections that spread out through the community, with few places where students directly shared the same partners with each other. But they were indirectly linked, partner to partner to partner. One component of the network linked 288 students – more than half of those who were romantically active at the school – in one long chain. (See figure for a representation of the network.)

James Moody, co-author of the study and professor of sociology at Ohio State University, said this network could be compared to rural phone lines, running from a long main trunk line to individual houses. As a comparison, many adult sexual networks are more like an airline hub system where many points are connected to a small number of hubs.

While many students were connected to much larger networks, they probably didn’t see it that way, Moody said. In fact, they probably had no idea of their connections to the network. “Many of the students only had one partner. They certainly weren’t being promiscuous. But they couldn’t see all the way down the chain.”

“We went into this study believing we would find a core model, with a small group of people who are sexually active,” Moody said. “We were surprised to find a very different kind of network.”

The results have implications for designing policies to stop the spread of sexually transmitted diseases among adolescents, he said.

The study was conducted by Peter Bearman of Columbia University, Moody, and Katherine Stovel of theUniversity of Washington. The results were published in a recent issue of the American Journal of Sociology.

The researchers used data from the National Longitudinal Study of Adolescent Health. As part of that study in 1995, researchers interviewed nearly all students at an unidentified Midwestern school that they renamed “Jefferson High School.” It is an almost all-white school, and is the only public high school in this mid-sized city, which is more than an hour away from the nearest metropolitan area.

Researchers interviewed 832 of the approximately 1,000 students at the school. Students were asked to identify their sexual and romantic partners in the past 18 months from a roster of other students attending their school. (Romantic relationships were ones in which the students named the other as a romantic partner. Non-romantic sexual partners were those in which the participants said they had sexual intercourse, but were not dating).

Slightly more than half of all students reported having sexual intercourse, a rate comparable to the national average. The researchers mapped the network structure of the 573 students involved in a romantic or sexual relationship.

Moody said the results generate a snapshot of the network of romantic and sexual relations among teens attending the school in this 18-month period –- the first such image of an entire population such as this.

The most striking feature of the network was a single component that connected 52 percent (288) of the romantically involved students at Jefferson. This means student A had relations with student B, who had relations with student C and so on, connecting all 288 of these students.

While this component is large, it has numerous short branches and is very broad – the two most distant individuals are 37 steps apart. (Or to use a currently popular term, there were 37 degrees of separation between the two most-distant students.)

“From a student’s perspective, a large chain like this would boggle the mind,” Moody said. “They might know that their partner had a previous partner. But they don’t think about the fact that this partner had a previous partner, who had a partner, and so on.

“What this shows, for the first time, is that there are many of these links in a chain, going far beyond what anyone could see and hold in their head.”

Outside of this large component, there were numerous other smaller components in the network at Jefferson High. There were 63 simple pairs – two individuals whose only partnership was with each other.
All told, only 35 percent of the romantically active students (189) were involved in networks containing three or fewer students. There were very few components of intermediate size (4 to 15) students.

While many students were connected to much larger networks, they probably didn’t see it that way, Moody said. In fact, they probably had no idea of their connections to the network.

“Many of the students only had one partner. They certainly weren’t being promiscuous. But they couldn’t see all the way down the chain.”

The surprising thing about the network at Jefferson High was the near absence of cycling –- situations in which people have relationships with others close to them on the network, Moody said.

The lack of cycling seems traceable to rules that adolescents have about who they will not date. The teens will not date (from a female perspective) one’s old boyfriend’s current girlfriend’s old boyfriend. This would be considered taking “seconds” in a relationship.

“If you break up with someone, you may want to get as away from them as possible in your next relationship. You don’t want to be connected to them in some way by dating someone with a close relationship,” Moody said.

The practical result from such a rule is that no cores form, and that long, chain-like networks form instead. That has important implications for preventing the spread of STDs in teenage populations, according to Moody, Bearman and Stovel.

In adult populations, in which there are cores of sexually active people who are the main conduits of disease, you can focus education and other efforts to this select group.

But in the case of adolescents, “there aren’t any hubs to target, so you have to focus on broad-based interventions,” Moody said. “You can’t just focus on a small group.”

This also means it matters less which people you reach with your efforts. Networks such as the one seen in Jefferson High are extremely fragile and just breaking one link in the chain – any link - will stop that part of the network from spreading any further. If enough links are broken, the spread of STDs can be radically limited.

“The students in this network are not unusual. They are just average students, and not extremely active sexually. So social policies that could help some of them protect themselves from STDs could break a lot of these chains that can lead to the spread of disease.”

#

Contact: James Moody, (614) 292-1722; Moody.77@osu.edu
Written by Jeff Grabmeier, (614) 292-8457; Grabmeier.1@osu.edu

Research News

Como en las investigaciones de redes sexuales, distribución de colas gordas (fat tail distribution)

Simplemente como un detalle anecdótico, esta encuesta publicada por el sitio Entre Mujeres sobre la cantidad de compañeros sexuales que han tenido una muestra de mujeres, responde a los patrones observados por otras investigaciones sobre redes sexuales en la cuales predominan un número muy pequeño de gente con muchos compañeros sexuales y una gran mayoría que solo tuvo pocos o ninguna pareja sexual. A esa distribución de eventos se le denomina distribución de ley de potencia o distribución de cola gorda.

3 de cada 10 mujeres perdieron la cuenta de con cuántas personas se acostaron

Desde Entremujeres quisimos saber la cantidad de amantes que tuvieron nuestras lectoras. Lanzamos una encuesta que demostró que, en tiempos de amores fugaces, el "touch and go" se lleva la delantera. ¿A vos tampoco te alcanzan los dedos de las manos para sacar el cálculo? 

¿Con cuántas personas tuviste intimidad? Simple y directa fue la consigna de la encuesta de Entremujeres, que contó con 22.560 participantes. Las respuestas fueron muy variadas y pasaron de un extremo al otro con total libertad. Pero una, la ganadora, llamó especialmente nuestra atención. 

“Perdí la cuenta”

Así nomás. El primer puesto se lo llevó el reino de los “touch and go”. Unas 6.435 mujeres (el 28,5% de las que votaron), ya ni recuerdan de la cantidad de personas con las que se fueron a la cama… O a lugares similares.

“De 2 a 5”

Ni nada ni demasiado. “De 2 a 5” fue la segunda respuesta con mayor cantidad de votos. La eligieron 5.725 mujeres, el 25,4% de las participantes. Ellas tienen experiencia en la cama, apuestan al erotismo, pero le huyen al amor descartable.  

“De 6 a 10”

Como era de esperar, la opción intermedia ocupó una posición intermedia. Fue elegida por 3.554 mujeres, el 15,8% de las participantes.

“De 11 a 20”

El mayor de los rangos propuestos quedó casi al final. Tuvo 3.455 votos, el 15,3% de los realizados. Quizás la fiaca pueda explicar el flojo resultado: ¿para qué ponerse a calcular si existe la opción “perdí la cuenta”?

“Solo con el amor de mi vida”

Si bien quedó última, la respuesta más romántica (y recatada) tuvo unos cuantos votos. Hasta podríamos decir que nos sorprendió. En tiempos de relaciones fugaces, unas 3.391 mujeres (el 15% de las encuestadas) siguen apostando a su primer y único amor.  

Y vos, ¿con cuántas personas te acostaste? ¿Fueron pocas y especiales? ¿O tenés que usar los dedos de varias manos para recordarlas?

Entre Mujeres

Una clase de Análisis de redes sociales avanzado usando R y Statnet

Goudreau-Hunter tutorial on Advanced Social Network Analysis Using R and statnet

Below are the videos for the Goudreau-Hunter tutorial on social network analysis using R. Note that this tutorial was recorded on five tapes of about one hour each, hence the five videos below.

"Advanced Social Network Analysis Using R and statnet"

Presenters
Steve Goodreau and David Hunter

Description
This workshop will introduce the use of the R statistical computing platform (via the statnet software suite) for statistical modeling of social network data. Topics covered include the use of exponential random graph (ERG or p*) models for representing structural hypotheses, model parameterization, simulation and inference, degeneracy checking, and goodness-of-fit assessment. Although a short "refresher" will be provided, some prior exposure to R and standard network analytic methods is strongly recommended.

For more information, please see the workshop web page, or our project home page .

Goudreau-Hunter Political Networks 2009 1 of 5 from David Lazer on Vimeo.

Goudreau-Hunter Political Networks 2 of 5 from David Lazer on Vimeo.

Goudreau-Hunter Political Networks 2009 3 of 5 from David Lazer on Vimeo.


Goudrieu-Hunter Political Networks 2009 4 of 5 from David Lazer on Vimeo.

Gooudreau-Hunter Political Networks 2009 5 of 5 from David Lazer on Vimeo.

Posted by David Lazer | Permalink 

Visualización: Nube de palabras de la conversación política estadounidense

Campaign visualizations: the a moving picture of the national conversation

I've been working with postdoctoral fellow of mine at Northeastern and IQSS, Yu-ru Lin, on visualizations that capture campaign 2010. Over the next couple of days we will be posting some of the visualizations on the blog. The first visualization is a dynamic word cloud based on daily snapshots of all Democratic and Republican campaign websites in October. So, for example, the words for the home pages of all Democratic candidates for the House were pooled together, and for each day, a word cloud was created, where words were sized based on their frequency (certain functional words were omitted, and word counts were normalized so no one website could dominate the count). This process was repeated for Republicans in the House, and for both parties in Senate and gubernatorial races. Below we show the dynamics for the Republican and Democratic websites. For the full set of 6 graphics, with interactivity, we have set up a dedicated website.

A brief perusal suggests some interesting contrasts. You can see jobs in both websites, but more prominently for Democrats, and tax and spending are a lot more visible for Republicans. America is big for Republicans, and education for Democrats. Democrats talk more about veterans and security, and Republicans about business. Republicans use "Republican" a lot, and Democrats "Democrat" very little. Notably missing are: Iraq, Afghanistan, health, and Obama. (For health, there is an interesting contrast with Senate campaign websites, where both parties feature health very prominently.)ç

Posted by David Lazer at 9:08 AM | Permalink

Una presentación sobre ciencia social computacional, por Lazer

PRESENTATION ON COMPUTATIONAL SOCIAL SCIENCE

This 10 minute presentation of mine on computational social science (using "big data" to understand social systems) at Harvard's CID might be of interest to some readers of this blog. It covers issues ranging from detecting emergencies in sociotechnical systems, to detecting "invisible" political networks from unstructured text:

A reminder-- if you are from the Northeast and have an Android phone, please participate in our study on communication behavior during Hurricane Sandy. We will be giving $3 to food banks in affected areas for every completed survey we receive.

By David Lazer | 2:59 PM

STUDYING HURRICANE SANDY VIA DATA ON _YOUR_MOBILE PHONE

My lab, with the support of the NSF, is launching a crowd-sourced study of Hurricane Sandy, so as to better understand how people react in emergencies. If you were affected by Hurricane Sandy and use an Android phone, I hope you will be willing to help out. This will take 10-15 minutes of your time. And if you weren't, then I hope you can pass this post on to someone that was affected by Sandy.

How do people respond in large-scale emergency situations, like earthquakes and hurricanes? Understanding this should inform more effective responses to save lives and reduce hardships. Getting hard behavioral data in the moment and aftermath is difficult--because people have better things to do than to participate in a study. There is quite a bit of valuable research based on interviews after the fact, but such research necessarily relies on reconstructed memories of behavior.

There is another path--which is to study the data passively collected about people by the sociotechnical systems relied upon during emergencies. An outstanding example of this is the paper by Bagrow et al that examined behavior as captured by mobile phones during a set of emergencies. The power of this approach is that it offers hard behavioral data at massive scale. The shortcoming, however, is that it cannot contextualize (beyond geography) the data. Who, exactly, are people calling? Their spouses? Friends? What are they communicating--the need for help, reassurances that they are ok?

Here we are launching a study that sits between these two approaches. Essentially, we are asking people to load an app on their Android phones (iPhone users: sorry, but for now we could only afford to develop for one platform), and the app will ask about their situations during Hurricane Sandy, and look at their calling and texting behaviors, asking them about their relationships with those individuals. We will therefore get a precise record of behaviors before/during/after Hurricane Sandy, and contextualize within personalize circumstances and particular relationships.

My motivation here is scientific and personal. I think there is the possibility to do great science here that is potentially consequential for people's lives, that can inform interventions that will help people. And, having grown up on Long Island, and spent the early part of my career Red Bank, New Jersey --near the shore ("shaw")-- I could see a lot of suffering occur among my friends and family in the aftermath, where there was very little I could do. But this study is at least something good that I can make out of a terrible thing.

We have posted more information about the study on our newly launched crowd-sourced science website, Volunteer Science, or you could go directly to the Google Play store.

By David Lazer | 

Computational Social Science

¿Papá, en que aplicación conociste a mamá?

After Tinder, kids of the future may be asking “Mommy, which app did you meet Daddy in?”

By Simone Foxman @simonefoxman

Short on time and savvy with social media, young professionals have been some of the first to adopt Tinder, a dating app launched in October. The app, available on iPhones and iPads, lets you decide—based on a few details and a couple of pictures—whether a person within a certain radius of you is “dateable.”

It works that out by comparing your lists of friends and interests on Facebook to match you with likely prospects. Then it shows you their pictures. The beauty of the app is its game-like speed: swipe left (no thanks) or right (mmm, cute)—or click the X or heart button—and move on to the next one. If both you and another person separately approve one another, the app lets you chat together.

It may sound like the ultimate in objectification, but young people see it as a practical way to get rid of the drama and possibility of rejection. “There’s really no rejection,” says Justin Mateen, the app’s co-founder. “You don’t have to do anything. It’s really instant gratification. And you don’t have to sell yourself, which removes some of the stigma of dating sites.” The average user is a 27-year-old iPhone owner in a big city, according to Tinder. The company plans an Android launch within the next 30 days.

Anecdotal evidence suggests that it’s particularly popular with people who spend long hours at work and have the least time for meeting people the old-fashioned way. ”With crazy schedules it was a fast way to meet someone,” says one private equity analyst. “The two people I’ve been on Tinder dates with are pretty impressive people career-wise.” A junior associate at a major Wall Street firm admitted that his friends are obsessed with it. Another said that using Tinder and OkCupid had developed into a “hobby.” This author even watched four of her male and female friends—twenty-somethings in finance and advertising—spend over an hour swiping away on the app at a bar last month. (Riveting.)

Rating would-be dates is also a group activity. And then the inevitable “OH! I know him/her” moment.Instagram

The adoption has been massive and global, according to the company. Tinder says it has made 60 million matches since October, its users have rated each other 5.7 billion times, and it has prompted 20 engagements. In some countries, Tinder users already number more than 1% of the country’s entire population. The more seasoned OkCupid—a property owned by social media conglomerate IAC, which was also an early investor in Tinder—asked Tinder for help on its own swiping “Hot or not” function, OkCupid Local. Since that app launched two weeks ago, its users have voted on each other 20 million times.

But Tinder plans to use its platform to expand beyond dating. “We don’t consider ourselves a dating app per se,” says Mateen. “We want to include everyone. The need to meet new people is universal…We don’t believe it’s right to restrict a relationship and categorize it. We feel like keeping it broad and people can figure out the intentions of others by chatting.” He said future versions of the app will be focused on including people who aren’t looking for a romantic relationship, and could include mobile purchases in some form.

Of course, Tinder’s future plans and monetary ambitions probably matter little to Tinder’s current users. The really burning question is: How many of them are devotees of Game of Thrones?

http://qz.com/95009

Mi precioso grafo social de Facebook

Facebook Is Done Giving Its Precious Social Graph To Competitors

By Josh Constine -  TechCrunch

Of all Facebook’s data sets, it’s the social graph that’s truly unique. It’s spent nine years getting you to confirm who you know, and apparently it’s sick of handing over your friend list to competitors. This week it cut off both Twitter’s new photo app Vine and messaging app Voxer from Find Friends, Facebook’s API that lets you connect with Facebook friends on other apps. But this could backfire.

Facebook knows who you are, what you’re interested in, where you go on the web, what apps you use, and more. However, other companies have bits and pieces of these data sets. LinkedIn knows your resume, Google knows your web searches, Twitter knows who you follow, Apple and Amazon have your credit card number, and your phone’s OS maker knows what apps you’ve downloaded. Who your real-life friends are, though, is Facebook’s domain.

Reconfirming your social graph manually on other apps is awkward at worst and annoying at best. Think about it. If your Facebook account were reset and you had to send friend requests to all your old friends, how many do you think would confirm? Even your best friends might be too lazy to, and people who were glad to friend you when you met years ago probably wouldn’t bother if they remember you.

There’s plenty of noise in Facebook’s social graph. Some people blindly accept most requests they get, others send them to anyone they meet once, and all the connections grow stale over the years. Still, if you want to jumpstart a social app, Facebook’s Find Friends feature is very valuable. It can be the difference between an empty feed and low retention, and a vibrant, addictive feed teeming with content from people you care about.

Facebook has offered Find Friends for years. But those were years when it was a web-based social network. It’s more now, or at least it wants to be. Facebook hopes to host all the ways you communicate. That has pitted it against Apple, Google, and other companies in war for messaging that’s only just heating up.

Data portability first became a big issue in 2010 when Facebook blocked Twitter from using its Find Friends feature. Later that year it got into a spat with Google about exporting contact lists. Google was pissed Facebook was sucking in Gmail contacts but not exporting friend lists. Facebook eventually began offering Download Your Data, which included your Friend List, but only in plain text.

More recently though, Twitter may have awoken the dragon when it cut off Instagram’s access to Twitter’s own version of “Find Friends.” It was an understandable retaliation since Facebook had cut it off, and Twitter had wanted to buy Instagram, too.

Now Facebook is coming out swinging, citing its Platform Policy that states “Competing social networks: (a) You may not use Facebook Platform to export user data into a competing social network without our permission.”

Last week it blocked voice-messaging app Voxer’s access to the social graph. At the time, Facebook told me this was because Voxer qualified as a competing messaging platform, but also because Voxer wasn’t contributing much back to Facebook. Voxer only had a buried, and largely unnecessary, “share to Facebook” option. It got an email stating its Find Friends access would be revoked 48 hours later.

Then today Facebook shut off Find Friends for Twitter’s Vine, as spotted by Jeff Martines and reported by The Verge. That makes both more and less sense. More because Twitter is a real competitor. It has decent scale and mindshare and competes for the same advertisers as Facebook. Twitter would love to know your Facebook social graph, which could help it refine its version, the “interest graph,” which powers its ad targeting.

It makes less sense because Vine had a prominent “share to Facebook” option. What happened to Facebook only going after apps that don’t contribute much back? Apparently that got overruled because Twitter is a more legitimate threat. So much so that Facebook employed its policy that “we reserve the right to take action against your app even before the end of this 48 hour period.” It didn’t want Twitter getting any social graph data.

Enforcement of these policies could create a moat around Facebook. It creates a barrier to engagement, retention, and growth for competing companies. It will force social apps to rely on other data sets, such as your phone’s contacts which may not have as complete of a social graph, though likely does include your closest friends whose numbers you have.

That advantage may not be worth it, though. The enforcement means Facebook is not an “open platform.” If companies are worried that Find Friends or other Facebook data access could be ripped away from them with little notice, it could cause a chilling effect on development on the Facebook platform. No one wants to build an app that relies on Facebook data if it could disappear.

Facebook reaffirmed this fear this morning when it enforced its ban on exporting data for use in social networks. Russian search engine Yandex’s new social search mobile app Wonder got all of its API calls blocked just three hours after launch. That’s a lot of programming and product work down the drain.

Facebook is playing with fire. It could use policy enforcement to cook competitors and shine a light on its dominance of social networking. But if this enforcement scares off developers whose apps might otherwise provide content that could be shown next to ads in the news feed and piped into Graph Search, Facebook could get burned badly.

[Image Credits: Newscom]

Como visualizar tu red de amigos de Facebook

How to: visualize the network of your friends on Facebook

Here I will teach you how to visualize the network of mutual friendship among your Facebook friends using a simple online tool called FriendsGraph.

First of all you will need to access the FriendsGraph website: https://app.friendsgraph.me and login using your Facebook account. You will be asked to allow the application to access the list of your friends but don't worry, it's just needed to build the network.

Now you are almost ready to visualize your network. FriendsGraph will take up to a couple of minutes to compute the connections among your friends, in the meantime you will be presented some interesting facts related to your friends.

After approximately one minute you will be able to explore your network of friends. You can zoom in and out using your mouse wheel or the bar on the bottom left of the screen. Clicking on a friends will highlight the sub-network of common friendship and display a picture of him/her. The search bar at the top of the screen can be used to search for a particular friend within your network.

The application has another very interesting feature: as you have noticed, FriendsGraph assign to each node (friend) a color based on a community-detection algorithm and a position based on the connections with the other nodes (friends). This means that your friends which belong to the same "social group" are more likely to be near and to be of the same color.

Are you able to distinguish the different groups among your friends and give a name to them?

- See more at: http://cavajohn.blogspot.com.ar/2013/06/how-to-visualize-network-of-your.html#sthash.l2Idq0hx.dpuf

Data Analysis and Visualization

El Poder De Las Redes Sociales (3/3) - Eduard Punset

El Poder De Las Redes Sociales (2/3) - Eduard Punset

El Poder De Las Redes Sociales (1/3) - Eduard Punset

La geografía de los Tweets

The geography of Tweets

By Miguel Rios (@miguelrios), Visual Insights Manager - Fuente

Give every Twitter user a brush and they will paint you the world — if they geotag their Tweets.

Those of us on the Visual Insights team are obsessed with the patterns that emerge from aggregated Tweets over time. A continuing curiosity is about the geographical shapes that surface in geotagged Tweets. The images we’re sharing here use all of the geo-tagged Tweets since 2009 — billions of them. (Every dot is a Tweet, and the color is the Tweet count.)

I’m especially fond of this view of Europe, because it shows all the maritime traffic from different cities and countries.

Seeing the clarity of the regional images led me to work up images for cities, too. Here are three favorites: Istanbul, Tokyo and New York. I like these in particular because of their uniqueness in data quality, leavened by my own qualitative taste. I hope you enjoy them. You can see more cities on our Flickr page.

Istanbul: 

Tokyo:

New York:

Topics: 

• geotagging
• and
• visualizations

Como la Agencia Nacional de Seguridad mapea redes de terroristas

How The NSA Uses Social Network Analysis To Map Terrorist Networks

Ever since The Guardian reported that the National Security Agency (NSA) has been collecting the phone record metadata of millions of Americans, the cable talk circuit has been ablaze with pundits demanding answers to what should be obvious questions.

Who knew about the program to collect data? (Apparently, all three branches of government). Who else has been supplying data?  (Just about everybody,according to the Washington Post). What is metadata?  (It’s data about data).

The question that nobody seems to be asking is probably the most important one:  What is the NSA doing with the data and why do they need so much of it?  The answer is a relatively new field called social network analysis and, while it may make people uneasy, the benefits far outweigh the risks, so it is probably something we will just have to accept.

The New Science of Networks

The story of networks starts in 1736, long before the United States became a country, when Leonhard Euler set out to conquer a famous math problem concerning the Seven Bridges of Königsberg. To solve it, he created a new form of mathematics called graph theory, which concerned itself with links and nodes in a network.

In the 1950’s, interest renewed in Euler’s networks.  First, Anatol Rapoport introduced the concept of triadic closure, which asserted that networks grow when people meet through a central friend that they both know.  Later, Erdős and Rényi showed that as networks got bigger, communication among the people in the network got much more efficient.

In the 1970’s a sociologist named Mark Granovetter argued that we get most of our information not through close friends, but through weak ties and in the 1990’s Watts and Strogatz built on Granovetter’s work by showing that small clusters of people naturally organize themselves into far flung networks.

So by the late 1990’s, the small field of network analysis had built into a full fledged science and it was about to be applied to an increasingly important problem:  Terrorist networks.

Mapping Terrorist Networks

Valdis Krebs of Orgnet is a network scientist who in 2002 published a widely praised paperon mapping terrorist networks and has since consulted with the Defense Department on methods and approaches of evaluating and dismantling terrorist organizations.

While he isn’t privy to classified information, he describes on his website how an entire network can be mapped using commercially available software by identifying two initial suspects:



It used to be that law enforcement officers would simply watch the two men closely, but in the era of global jihad, that’s much too slow to save lives.  The two might be peripheral to the conspiracy and it could take years before you could connect them to the leadership of the network, if ever.

Here’s where the data from Verizon and other companies comes in.  If you can analyze communication records, you can move much more quickly.  However, you don’t want to look at everyone the suspects talk to because you’ll end up with mostly incidental contacts, like friendly neighbors and delivery men.

But if you kept Rapoport’s concept of triadic closure in mind and had full access to communication records, you could look for contacts the two suspects have in common and start to build out a map of the conspiracy.

The next step would be to analyze the contacts of the suspects’ connections, again looking for closed triads within the existing network.  As you progress from link to link, a fuller picture begins to form (click to enlarge).

 

Once you have the network mapped, you can begin to mathematically analyze it, which is how important insights can be gleaned even before wiretapping and surveillance warrants have been issued.

You can, for example, assess who is well integrated into the network by calculating who is most central; who has the widest reach by counting how many people in the network are within two connections from them and who in the network provides a crucial role as a bridge between otherwise unconnected people (as Mohamed Atta, the uppermost green node, does in the 9-11 network above).

The result is an almost uncannily accurate picture of the leadership, who can then be targeted to dismantle the network. (It has been estimated that the 9-11 network could have been broken up if just three central nodes had been taken out).

It should be clear by now why the government regards access to communication records as so crucial to national security.  If the system had been in place in 2001, there is a high probability that the 9-11 network would have been broken up, saving thousands of lives and trillions of dollars.

It would be impractical, to say the least, to get court orders for each and every connection a suspect has, most of whom would not even be investigated. Without a full data set, the social network analysis could not be done and more intrusive, but less efficient methods, would need to be employed.

So whatever you might think of the program, it is most probably here to stay. What is perhaps of greater concern is that this type of analysis is not unique to antiterrorism, but is increasingly becoming a basic fact of commercial life.

Beyond the cell phone companies, social networks like Facebook, Google+ and Twitter can analyze the communications of hundreds of millions of people.  Retail giants like Amazon, Walmart and Target are sifting through our purchases in order to predict our future behavior.

Wherever we go, our movements, faces and actions are being analyzed and, more often than not, it is not the government.

The truth is that there is a dark side to technology and our privacy is being breached every day by someone, somewhere.  That’s just a fact of modern day life.  It seems to me that if we’re willing to accept it from marketers who are trying to to sell us goods and services, we should be able to tolerate it from those who are trying to protect us.

- Greg

Note:  Special thanks for Valdis Krebs of Orgnet for supplying the network maps, calculations and consultation for this post.

Digital Tonto

Redes de curriculums

How to Tap Your LinkedIn Network for Your Next Opportunity

Lindsey Pollak, Blog Linkedin

A recruiter once told me that he always has two stacks of resumes on his desk: one super tall stack of resumes he receives unsolicited from the Internet and one very short stack of resumes that have been passed along from people he knows and trusts.

Not surprisingly, when this recruiter has a job to fill, he reaches for the smaller stack of referred applicants first. It’s simply more efficient and effective to check out candidates who come with a recommendation from a trusted friend or colleague.

So, how do you get your resume into the coveted short stack? Here are some tips:

1. Put people first.

Instead of starting your job search with job postings, start with the people you know. Where do they work? Where did they used to work? Who do they know? What advice and introductions can they provide?

The new LinkedIn Jobs makes this easy by showing you all of the companies (that are currently hiring) where you have LinkedIn connections. Just scroll down the main Jobs page to “Jobs in Your Network” and start browsing opportunities. You can also visit the LinkedIn Company Pages of the organizations on your prospect list and view anyone in your network who currently works or used to work there.

2. Gather information and build relationships.

Now it’s time to reach out. Before you apply for any position, try to speak with or have an email exchange with someone who has worked or currently works for the employer — in other words, conduct informational interviews. Your goal in these conversations is not to ask for a job or even to ask for your resume to be passed along. Not yet. Your objective at this point is to gather information that will help to: 1) give you an edge when you do apply for a position, and 2) build a relationship with the person providing the information so that in the future this person will make a referral.

Here is what you might say in an outreach email to an existing connection:

Hi Rachel,

I hope all is well – I loved the recent article you posted on LinkedIn about the new iPhone. As you may know, I am in the midst of a job search and I was wondering if you would be willing to provide a bit of guidance. I am very interested in a position in IT support at Nike and I know that you worked there for several years. Would you be open to a brief phone chat or to answer a few questions by email to provide some insight into how Nike hires and what they look for? I would be so grateful for your time and very happy to return the favor.

Thanks,

Lindsey

Here is what you might say in an outreach email to a friend-of-a-friend (second degree connection):

Hi Roger,

I hope all is well. As you may know, I am in the midst of a job search for a senior communications position and I’m in the process of researching potential employers. I saw on LinkedIn that you are connected to Joan Harris, who works for Red Hat, a company I deeply admire and would love to work for. Joan is very active on LinkedIn and posts great content about the company, so I’m hoping she might be open to a chat with a potential applicant. Would you be willing to make an introduction? I would be grateful for your help and of course happy to help you in any way I can.

Thanks,

Lindsey

You’ll want to send as many emails like this as possible to increase your chances of gathering good information. Notice how important it is to review each person’s LinkedIn profile before reaching out so you can customize your request and prepare yourself for any conversation the person agrees to.

When people do agree to speak with you, here are some questions you might ask current or former employees of your prospect companies:

• May I tell you a bit about my background [if you don’t know the person well] and why I think this position and company would be a good fit? (It’s important that the person knows about you and your goal so you can focus the conversation.)
• How did you land your position at the company?
• Would you share any tips about how to stand out in the application or hiring process? (For example, what to include/not include on my resume or LinkedIn profile, what to say in my cover letter, when and how to follow up)
• Would you be willing to review my resume or LinkedIn profile and provide any feedback? After I make any of your suggested changes, would you consider passing my information along to the recruiter or [if the person no longer works at the organization] to someone you know at the company who might be willing to pass it along?
• Is there anything I can do to help you?

If your contact agrees to refer you or your information to a recruiter, you will want to send an immediate thank you message. Then be sure to keep this person in the loop as your candidacy progresses by providing occasional updates on the process and, of course, another thank you note if you land the job.

If your contact is not willing to pass along your resume or LinkedIn profile, that might be a sign to you that 1) your credentials are not a good fit for the organization, 2) this person may not yet know you well enough to make a referral, or 3) this person is just not that helpful (hey – it happens). No matter the reason, go ahead and move on to other prospects.

3. Maintain your connections.

Since no single conversation or referral is guaranteed to lead to a job, you’ll need to continue this process of reaching out to contacts, asking for advice and keeping people posted on your progress. One great new way to keep all of your relationships active and strong is to use LinkedIn’s new Mentions feature.

Mentions makes it quick and easy to have brief conversations with the people in your network by allowing you to directly reference a contact in your LinkedIn status updates on the homepage. The person you mention will receive a real-time notification, which will keep you on his or her radar screen and promote ongoing discussion – which could lead to an opportunity.

You might use mentions to call attention to an article that would interest one of your recent informational interviewees, congratulate a connection on a new job or promote a company’s status update that celebrates an achievement by one of your connections.

The goal of using mentions in this situation is to keep in touch with your connections without being overly aggressive. And, as always, remember that the more you help and support people in your network, the more likely they are to want to help you in return. That’s what it means to tap the referral network.