Leskovec is an assistant professor of computer science at Stanford University. The focus of his research is on the traces human activity leaves on the Internet. Someone reads an article on a news website, writes a blog post or forwards someone else's tweet on Twitter: It is data like this that Leskovec collects to analyze human behavior on the Web -- and even predict it. He does the same with data from social networks.

"Data shows that who will be our next friend on Facebook is not so random as we think," he said. He has just finished a project with the company that runs the social networking site. Based on information about the personal networks of users and their communication he was able to tell in advance half of the new contacts they would add shortly after.

In the future the rate of correctly predicted new friends could be even higher, he said. "We are able to train the analyzing methods," Leskovec said.

Findings like this could be used to develop models of how online groups grow over time. "It will soon tell us how healthy a community is," said Leskovec. So far his analysis shows that a social network should neither be too sparsely populated nor too dense. "Our research suggests it may not be good to saturate a network," he said. This means too many contacts and too much communication could someday thwart vitality and growth in a network.

Another project Leskovec just finished was an analysis of the user community of Microsoft Instant Messenger. The research project in cooperation with Microsoft proved the hypothesis of the "6 degrees of separation." Leskovec found that people using Messenger were in general 6.6 steps apart from each other. While this was fundamental research, dealing with questions like this could also enable practical applications, Leskovec said.