Degrees of separation: A variety of different social networks can be generated by altering how connections between users are defined.
Incomplete information can throw off attempts to characterize social networks automatically, says Eric Gilbert, who will be an assistant professor of interactive computing at Georgia Tech starting this fall. Algorithms can miss identifying the most intimate connections because these are likely to be face-to-face rather than digital communication–what Gilbert calls it the “spouse problem” or “the roommate problem.”
Gilbert has found that studying the structure of a network in greater detail can compensate for this to a degree. For example, a married couple is likely to share a large number of friends. But he acknowledges that this doesn’t solve the problem altogether.
On the flip side of the spouse problem is “the ex problem,” which was highlighted during the launch of Buzz. This occurs when algorithms connect two people who may have communicated frequently at one point but no longer do, and no longer wish to–such as estranged romantic partners. Gilbert explains that it’s hard to automatically discover an event such as a breakup because of the complex variables that surround it. Two people may stop communicating because one is busy, or on vacation. Algorithms would have to examine and compare complex behavior over time and in the context of other connections to understand this.
Munmun De Choudhury, who was involved with the Yahoo research and now works at Microsoft Research, says that more research can be done to help algorithms better understand the nature of social connections. Frequent e-mails could indicate either a very positive or very negative relationship, for example, and additional analysis might help algorithms identify the difference between the two.
Ultimately, Adamic says, it is a question of how much error can be tolerated when generating a network automatically. In some cases, algorithms that mine e-mail and other communications work quite well, and can be used to save time by providing an overview of connections or filtering information.
Automatically inferring the nature of social connections may be useful for prioritizing messages or establishing privacy settings that a user could then approve. However, “you don’t want to overinfer or get so fine-grained that it’s creepy,” Gilbert cautions.
All the researchers agree that allowing users to clean up any errors introduced by the algorithms is crucial to progress. “You always have the option of bringing in the human element,” says Adamic. “You could always take a step where the algorithm is 95 percent accurate and you let individuals handle the last 5 percent.”