One of the problems we often encounter with our social networks is the lack of “fuzziness” that they provide us with respect to our relationships—that is, with standard social networks you either have a relationship with another person, or you don’t. I discussed this issue in The Learning Layer:
People clearly comprise networks, and the relationships between people are not necessarily just digital in nature. We all have some relationships that are very strong, and others that are much weaker. Some people are our soul mates, some are friends, some are colleagues, and some are just acquaintances. There are shades of gray in our social relationships, just as in the case of relationships among items of content and topics. And there are different types of relationships among people, and among people and content that should be explicitly recognized. Some of these types of relationship may, in fact, be digital—for example, someone is your classmate or is not; someone is an author of an item of content or is not. But some types of relationships, such as the degree of similarity of preferences between two people, or the degree of interest a person or a group of people have with regard to a topical area, clearly will not be digital. They will be much more nuanced than that.
The inability to manage our online relationships in a more nuanced (i.e., fuzzy) fashion leads to ever bigger headaches as the scale of our social networking connections (i.e., our “social graphs”) increase. I had some comments on the way social networks have attempted to address this problem in the blog post, Social Networking and the Curse of Aristotle. At the end of the post, I mentioned that leveraging the power of machine learning provides a way for us to share activities and information in better accordance with the specific wishes we would have if we actually had the time to fully consider whether to share a particular item with each specific person to whom we are connected.
Along these lines, a recent study confirms just how well our interactions within a social network (e.g., Facebook) can be used to infer the strength of our real-world relationships. And, in fact, under the covers, Facebook’s algorithms already use this type of information to decide what to deliver in your feeds and what not to deliver. Likewise, Synxi learning layer apps do something quite similar in recommending other users or their content to users of enterprise social platforms. So machine learning is already on the job for you—your social graph is fuzzy, whether you know it or not!