The algorithm, developed by Jon Kleinberg, the Tisch University Professor of Computer Science, and colleagues, works best when the couple is married and works better the longer the relationship has been in force.

If the algorithm does not select the person who is the relationship partner, there is a significantly increased chance that in a month or two the couple will break up, researchers claim.

The researchers tested their methods on anonymised data from 1.3 million randomly selected Facebook users aged 20 or older, who listed their status as 'married', 'engaged' or 'in a relationship', according to 'Cornell Chronicle'.

Researchers first guessed that the romantic partner would be 'embedded' - that the couple would have many mutual friends. That worked, the researchers found, but not very well, they only found the partner about 25 percent of the time.

So they introduced a concept they called 'dispersion', where the couple's mutual friends are not highly connected among themselves but rather are scattered over many aspects of the central user's life.

In real-world terms, your spouse goes where you go, and knows the people in your office, your church, your bridge club and so on, although those people seldom meet one another across group lines.

Combining embeddedness with dispersion boosted performance. The researchers then factored in the dispersiveness of the dispersed friends - whether the person your romantic partner knows at your office is also connected to some people in your church and your bridge club.

Finally, they added measures of interaction, such as how often people look at each other's profiles, attend the same events or appear together in photos.

Ultimately they were able to identify the partner 70.5 per cent of the time. Others who might be chosen by the algorithm are most often family members or their partners.

The researchers were also able to determine, 68.3 percent of the time, whether a given user was or was not in a relationship at all, and with 79 percent accuracy if the relationship was a marriage.

(Agencies)

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