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Posts from the ‘Fuzzy’ Category

Our Fuzzy Social Graphs

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!


Social Networking and the Curse of Aristotle

The recent release and early rapid growth of Google+ has mostly been a direct consequence of social networking privacy concerns—with the Circles functionality being the key distinguishing feature versus Facebook.  Circles allows for a somewhat easier categorization of people with whom you would like to share (and gratefully only you see the categorizations in which you place people!).

What people rapidly find as their connections and number of Circles or Facebook Lists grow, however, is that the core issue isn’t so much privacy per se, but the ability to effectively and efficiently categorize at scale. A good perspective on this is Yoav Shoham’s recent blog on TechCrunch about the difficulties of manual categorization and his experience trying to categorize 300+ friends on Facebook. Circles is susceptible to the same problem—it just makes it easier and faster to run head-long into the inevitable categorization problem.

A root cause of the problem, as I harp on in The Learning Layer, rests with that purveyor of what-seems-to-be-common-sense-that-isn’t-quite-right, Aristotle.  Aristotle had the notion that an item must either fit in a category or not.  There was no maybe fits, sort of fits, or partly fits for Aristotle.  And Google+ (like Facebook and most other social networks) only enables you to compartmentalize people via the standard Aristotelian (i.e., “crisp”) set. A person is either fully in a circle/list/group or not—there is no capacity for partial inclusion.

But our brain more typically actually categorizes in accordance with non-Aristotelian, or “fuzzy” sets—that is, a person may be included in any given set by degree.  For example, someone may be sort of a friend and also a colleague, but not really a close friend, another person can be a soul mate, another mostly interested in a mutually shared hobby, etc. Sure, there are some social categories that are not fuzzy—either a person was your 12th grade classmate or not—but since non-fuzzy sets are just a special case of the more generalized fuzzy sets, fuzzy sets can gracefully handle all cases. So fuzzy sets have many advantages and this type of categorization naturally leads to fuzzy network-based structures, where relationships are by degree.  (The basic structure of our brain, not surprisingly, is a fuzzy network—the structure I therefore call “the architecture of learning” in The Learning Layer.)

But an issue with implementing in a system the reality of our social networks as fuzzy networks is that it can be hard to prescribe ahead of time sharing controls for fuzzy relations.  If we actually bothered to decide on an individual basis as to whether to share a specific item of content or posting, we would naturally do so on the basis of our nuanced, fuzzy relationships.  But that, or course, would take some consideration and time to do.

So the grand social networking bargain seems to be that for maximum expedience we either resign ourselves to share everything with everyone (what most people do on Facebook), or we employ coarse-grained non-fuzzy controls (e.g., Circles, Lists) that are a pain to set up, imprecise, and don’t scale.  Or there is another option—we cast Aristotle aside and establish and/or let the system establish a fuzzy categorization and then let our system learn from us to become an intelligent sharing proxy that shares as we would if we had time to consider fully each sharing action.  That will, of course, require trusting the system’s learning, which will necessarily have to be earned.  But ultimately that approach and the sharing everything with everyone are the only two alternatives that are durable and will scale.