A new study shows that your chances of forming online friendships depends on the number of groups and organizations you join, not their types.
“If a person is looking for friends, they should basically be active in as many communities as possible,” said Dr. Anshumali Shrivastava, an assistant professor of computer science at Rice University in Houston and co-author of the study. “And if they want to become friends with a specific person, they should try to be a part of all the groups that person is a part of.”
The study’s findings are based on an analysis of six online social networks with millions of members. Shrivastava noted that its simplicity may come as a surprise to those who study friendship formation and the role communities play in bringing about friendships.
“There’s an old saying that ‘birds of a feather flock together,'” Shrivastava said. “And that idea — that people who are more similar are more likely to become friends — is embodied in a principal called homophily, which is a widely studied concept in friendship formation.”
One school of thought holds that because of homophily, the odds that people will become friends increase in some groups, he explained. To account for this in computational models of friendship networks, researchers often assign each group an “affinity” score. The more alike group members are, the higher their affinity and the greater their chances of forming friendships, he noted.
Before social media, there were few detailed records about friendships between individuals in large organizations. That changed with the advent of social networks that have millions of members who are often affiliated with many communities and subcommunities within the network, according to the researchers.
“A community, for our purposes, is any affiliated group of people within the network,” Shrivastava said. “Communities can be very large, like everyone who identifies with a particular country or state, and they can be very small, like a handful of old friends who meet once a year.”
Finding meaningful affinity scores for hundreds of thousands of communities in online social networks has been a challenge for analysts, the researchers said. Calculating the odds of friendship formation is further complicated by the overlap between communities and subcommittees.
For instance, if the old friends in the above example live in three different states, their small subcommunity overlaps with the large communities of people from those states. Because many individuals in social networks belong to dozens of communities and subcommunities, overlapping connections can become dense.
In 2016, Shrivastava and study co-author Chen Luo, a graduate student in his research group, realized that some well-known analyses of online friendship formation failed to account for any factors arising out of overlap.
“Let’s say Adam, Bob, and Charlie are members of the same four communities, but in addition, Adam is a member of 16 other communities,” Shrivastava said.
“The existing affiliation model says the likelihood of Adam and Charlie being friends only depends on the affinity measures of the four communities they have in common. It doesn’t matter that each of them are friends with Bob or that Adam’s being pulled in 16 other directions.”
That seemed like a glaring oversight to Luo and Shrivastava. But they had an idea of how to account for it based on an analogy they saw between the overlapping subcommunities and the overlapping similarities between webpages that must be taken into account by Internet search engines.
The researchers were able to measure the overlap between communities. They then checked to see if there was a relationship between overlap and friendship probability, or friendship affiliation, on six well-studied social networks.
They found that on all six, the relationship more or less looked like a straight line.
“That implies that friendship formation can be explained merely by looking at overlap between communities,” Luo added. “In other words, you don’t need to account for affinity measures for specific communities. All that extra work is unnecessary.”
Once Luo and Shrivastava saw the linear relationship between the overlap of communities and friendship formation, they also saw an opportunity to use a data-indexing method called “hashing,” which is used to organize web documents for efficient search.
Shrivastava and his colleagues have applied hashing to solve computational problems as diverse as indoor location detection, the training of deep learning networks, and accurately estimating the number of identified victims killed in the Syrian civil war.
Shrivastava said he and Luo developed a model for friendship formation that “mimicked the way the mathematics behind the hashing work.”
The model offers a simple explanation of how friendships form, he reported.
“Communities are having events and activities all the time, but some of these are a bigger draw, and the preference for attending these is higher,” Shrivastava said.
“Based on this preference, individuals become active in the most preferred communities to which they belong. If two people are active in the same community at the same time, they have a constant, usually small, probability of forming a friendship. That’s it.”
The study was presented at the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining in Barcelona, Spain.
Source: Rice University