Blog Post

Reasons why the social graph deserves to die

Stay on Top of Enterprise Technology Trends

Get updates impacting your industry from our GigaOm Research Community
Join the Community!

Maciej Ceglowski under CC license by Joi ItoIf you’ve ever gotten a little creeped out by the way social networks have invaded our lives, then you aren’t alone. There are a lot of people who enjoy using the social web, but struggle with it too.

Unfortunately, most of the rhetoric about this part of the web is still pretty uncomplicated, broadly split between those who gleefully champion the new openness and those who deride it as meaningless or destructive. That leaves many of us who have more complicated feelings stranded in the middle.

Good news, then, for this silent majority: perhaps we have a new manifesto in The Social Graph is Neither.

Written by Maciej Ceglowski, the founder of bookmarking website Pinboard, it’s a great essay that covers a lot of ground about the problems today’s social networks have, and I’d urge you to read the whole thing.

Broadly, he argues that the way social networking has developed — the technologies and the techniques — mean that the services we see today are really just a shadow of what the social web really is. Because they are based on an impossibly contrived and complicated idea, they will eventually be overtaken by something better:

Right now the social networking sites occupy a similar position to CompuServe, Prodigy, or AOL in the mid 90’s. At that time each company was trying to figure out how to become a mass-market gateway to the Internet. Looking back now, their early attempts look ridiculous and doomed to failure, for we have seen the Web, and we have tasted of the blogroll and the lolcat and found that they were good. […]

My hope is that whatever replaces Facebook and Google+ will look equally inevitable, and that our kids will think we were complete rubes for ever having thrown a sheep or clicked a +1 button.

More particularly, as the title suggests, Ceglowski argues that the “social graph” — a term first popularized by Facebook to describe the way we map relationships — is troubled for several reasons. Most straightforwardly, it is not a graph (it’s an impossibly complicated spaghetti of connections) and also because it’s not really social (at least in any way ordinary people recognize):

You might almost think that the whole scheme had been cooked up by a bunch of hyperintelligent but hopelessly socially naive people, and you would not be wrong. Asking computer nerds to design social software is a little bit like hiring a Mormon bartender. Our industry abounds in people for whom social interaction has always been more of a puzzle to be reverse-engineered than a good time to be had, and the result is these vaguely Martian protocols.

But he doesn’t just pick on the semantics. The essay also examines the processes that are used to map relationships, such as FOAF, and the way they have evolved over time, to look deeper.

The real problem with the social graph, he argues, is that it’s based around a series of troubling assumptions — including the idea that we can and should model human relationships, and for profit. As he says, “Imagine the U.S. Census as conducted by direct marketers – that’s the social graph.”

This is partly because the social web has really been spun off from the idea of the semantic web, and ways of describing connections between data that require all kinds of sleight-of-hand to work. How do you interpret messy relationships into things computers can understand, or translate meanings that are complex and constantly moving?

Machines will always struggle with complexity, particularly when it can operate at a very individual level. Is my definition of “enemy” the same as your “nemesis”? Are they very different? Are they exclusive? Some relationships never really shift (my brother is still my brother, even if we fall out) while some change over time (my brother-in-law might not always be my brother-in-law).

But even worse, some change in context: we might be drinking buddies, but I don’t want to hang around with you at the company picnic; or you might even be a bit of a douchebag when you’re stressed and while we are friends most of the time, I’d rather avoid you when you have an exam coming up.

This is what leads Ceglowski to argue that social networking of this sort is actually unsocial: “How are you supposed to feel at home when you know a place is full of one-way mirrors?” he points out.

You can take all of this with a pinch of salt. Pinboard is notorious for being an anti-social (although it does have social features) and Ceglowski himself points out he worked on LOAF, a rival to the previously mentioned Friend-of-a-friend project.

But he’s certainly right that mapping this stuff is very difficult, and perhaps impossible. It’s also the case that while we tend to think of this as a new struggle, this problem is not new to technologists, even though many of them would like to think so: sociologists have been struggling with the ways to represent relationships for years.

The real difference, however, is that while sociologists try to come up with ways to define interaction, technologists end up building systems that define the interactions that can happen. That means the processes behind today’s biggest social networks actually place themselves as a layer over human activity, as much as they help that activity exist.

It doesn’t have to be that way.

I think some of the greatest, most influential pieces of the social web — services like Blogger, Reddit, LiveJournal, and so on — aren’t determinative in the same way as Facebook is. They are surprising, emergent and unstructured, just like the Web itself.

This conflict is, I think, why Facebook is constantly struggling with privacy issues, or why the real names controversy on Google+ exploded. The social graph, to them, is an attempt to codify what people do rather than act as midwife to their ideas.

This, Ceglowski suggests, can’t go on forever. And it means that the social graph will look like a relic in a few years time. I hope he’s onto something.

Photography used under Creative Commons license courtesy of Joi Ito

4 Responses to “Reasons why the social graph deserves to die”

  1. Robert Carr

    A lot of good points. However, a lot of social networking does make sense… for certain people. Perhaps not the hopelessly asocial geek who is essentially trying to calculate why he isn’t popular. And perhaps not for those of us who are either too introverted or too not with it to get it. There is, though, a large chunk of the population who are sufficiently extroverted or gregarious or voyeuristic or exhibitionistic that Facebook/MySpace/Twitter/All The Rest all make perfect sense. Perhaps the question isn’t really whether “The Social Graph Deserves to Die” but what is a more universal substitute, like the post-Prodigy/Compuserve internet, for empowered social connection.

  2. In science there is different view emerging.

    In this study, Yuichi Yamashita and Jun Tani demonstrate that even without explicit spatial hierarchical structure a, functional hierarchy can self-organize through multiple timescales in neural activity. Their model was proven viable when tested with the physical body of a humanoid robot.

    Results suggest that it is not only the spatial connections between neurons, but also the timescales of neural activity, that act as important mechanisms in neural systems.[2]
    Neurons discriminate among signals based on the signals’ “shape,” (how a signal changes over time), and Forger and coauthors found that, contrary to prior belief, a neuron’s preference depends on context. Neurons are often compared to transistors on a computer, which search for and respond to one specific pattern, but it turns out that neurons are more complex than that. They can search for more than one signal at the same time, and their choice of signal depends on what else is competing for their attention.[1]

    “Second, we found that the optimal stimulus is context-dependent,” he said, “so the best signal will differ, depending on the part of the brain where the implant is placed.” [1]

    To recap:
    Information is data in context
    Context is organized data
    Learning is the self-organization of data [forms context]

    So yes systems can do this, it’s based on timing. I call it occurrence based data binding[data binding in space time] and bollean is just sub-set. In other words we got the math and physical evidence to do what needs to/can be done.