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   It’s not in the stars after all. Instead, it seems, the shape of a person’s social network is a powerful signal that can identify one’s spouse or romantic partner — and even if a relationship is likely to break up. 
   
   So says a new research paper written by Jon Kleinberg, a computer scientist at Cornell University, and Lars Backstrom, a senior engineer at Facebook. The paper, posted online on Sunday, will be presented at a conference on social computing in February. 
   
   The pair used a hefty data set from Facebook as their lab: 1.3 million Facebook users, selected randomly from among all users who are at least 20 years old, with from 50 to 2,000 friends, who list a spouse or relationship partner in their profile. That makes for a lot of social connections to analyze, roughly 379 million nodes and 8.6 billion links. The data was used anonymously. 
   
   Their key finding was that the total number of mutual friends two people share — embeddedness, in social networking terms — is actually a fairly weak indicator of romantic relationships. Far better, they found, was a network measure that they call dispersion. 
   
   This yardstick measures mutual friends, but also friends from the further-flung reaches of a person’s network neighborhood. High dispersion occurs when a couple’s mutual friends are not well connected to one another. 
   
   In the graphic of one person’s network neighborhood (above), the cluster at the top is the individual’s co-workers. The cluster at the right is old college friends. The node (friend) in the lower left quadrant of the graphic, with links to the two dense clusters — but at a distance from those clusters — is the user’s spouse. 
   
   “A spouse or romantic partner is a bridge between a person’s different social worlds,” Mr. Kleinberg explained in an interview on Sunday. 
   
   Their dispersion algorithm was able to correctly identify a user’s spouse 60 percent of the time, or better than a 1-in-2 chance. Since everyone in the sample had at least 50 friends, merely guessing would have at best produced a 1 in 50 chance. The algorithm also did pretty well with people who declare themselves to be “in a relationship,” correctly identifying them a third of the time — a 1 in 3 chance compared with the 1 in 50 for guesswork. 
   
   Particularly intriguing is that when the algorithm fails, it looks as if the relationship is in trouble. A couple in a declared relationship and without a high dispersion on the site are 50 percent more likely to break up over the next two months than a couple with a high dispersion, the researchers found. (Their research tracked the users every two months for two years.) 
   
   For Facebook, the research is part of its automated efforts to look more closely at the relationships among its users to tailor content and ads. Mr. Backstrom is the engineering manager in charge of Facebook’s News Feed, which delivers content from a user’s friends. 
   
   The more Facebook knows about a user’s relationships, the more appropriately tailored the News Feed can be. Do you want to see pictures of a child’s swimming lesson yesterday? Probably yes, if it’s from a family member or close co-worker, but probably not from someone on the fringes of your network of 2,000 “friends.” 
   
   So much of social-network analysis confirms what we already know. Relationships that last are ones in which the other person widens our world? Well, yes. Still, it’s kind of nice to have it confirmed with lots of data and algorithms. “We hadn’t had this view of it before,” Mr. Kleinberg observed. ||||| It might seem reasonable then if "embeddedness" could be used to predict people's plus-ones. But that's not what researchers Lars Backstrom of Facebook and Jon Kleinberg of Cornell University found. In a new paper , they write that embeddedness is an at best mediocre predictor of that special something. Relying on embeddedness, they were able to accurately predict Facebook users' significant others 24.7 percent of the time. 
   
   Here's what that means: The number of mutual friends ("embeddedness") is a reliable indicator of how close two people are. Simply put: You have more friends in common with your closest friends than with your acquaintances. 
   
   Facebook data scientists have developed a novel method for identifying who among a user's friends is that person's partner—and their work puts an empirical stamp on something that is perhaps intuitive: A significant other occupies a unique place in a person's social network, one characterized not by "embeddedness"—the standard way of measuring a tie's proximity—but by what the researchers call "dispersion." 
   
   Another measure fared much better: "dispersion," or how many different networks of theirs a person's friend shares. In other words, your significant other won't just share many friends with you, but friends from all walks of life: your colleagues, your high school buds, your college friends, your family, and so on. Using dispersion, Backstrom and Kleinberg doubled their accuracy: 50 percent of the time, a person was romantic partners with the person who was the most dispersed across his or her social network. For married people, their accuracy rose to 60 percent, a figure which they say is more than 30 times higher than random guessing would produce (everyone in their sample had at least 50 friends). 
   
   The researchers were able to work with their findings to produce a telling observation about the health of a given relationship: The more dispersion a relationship demonstrated, the more likely it was that that relationship would still be around 60 days later (the researchers excluded married couples from this analysis, though that would be quite interesting to see too). This is not necessarily causal (no, you can't get your girlfriend to stick around by bringing her to the company holiday party), but couples who are building a life together, as demonstrated by their multiple and interconnected social circles, seem to have more staying power. (Presumably, though no measurement tools have been developed yet, the breakups of such relationships are also the most painful.) 
   
   For now, researchers relied on looking at Facebook users who identify their partners on the site, but, as this sort of analysis gets refined, it's easy to see how they could use their methodology to detect a relationship which is not quote-unquote "Facebook official." Such information will be useful to Facebook in prioritizing certain people in your newsfeed, showing you those—and especially the one—you care most about.
target: – Facebook data scientists can figure out who you're sleeping with, even if your relationship isn't "Facebook official," and in a new paper researchers explain how they figured out the method, the Atlantic reports. Turns out the key is to look at more than just mutual friends. "Embeddedness," or the number of mutual friends two people have, can show how close those people are (the more mutual friends, the closer they are). But researchers found embeddedness predicted the correct significant others just 24.7% of the time. A more accurate predictor is something called "dispersion." That means researchers looked at how many networks were shared between two people, working on the theory that your romantic partner will have met your family and your current friends and co-workers, as well as old friends from high school or college. By looking at which friend was the most dispersed across all of a Facebook user's networks, researchers upped their accuracy at predicting significant others to 50%. "A spouse or romantic partner is a bridge between a person’s different social worlds," one researcher explains, according to the New York Times. Oh, and dispersion also works to predict the health of your relationship: The more dispersion a user had with his or her partner, the more likely the relationship was to last at least 60 days.