In recent years, the topic of social networking has gained a considerable amount of buzz. I wish a week could go by without someone showing me yet another graph visualization of an individual’s contacts and concepts they share with others.

I approach this kind of exploration with a more fundamental reasoning which purports that the most efficient social network is powered by people. We do not know who we do not know someone else knows and that is one of the reasons people are fascinated with social networking visualizations.

If you could show me the people my friends know and their friends know, I would know so many more people! On the other hand, deliver semi-random connections, like the introductions from Monster.com, and they are as good as or better than the contacts resulting from people who know people I know. Is it possible a semi-random introduction is as valuable as an introduction that was sent through a social network meat grinder?

Consider friends-of-friends and so on, and my list of potential contacts grows exponentially with very little effort. People marvel at the volume of connections six degrees of separation yields. How valuable are contacts that do not know me and know someone I do not? It is not about the network. It is about the people acting in the network.

I have participated in social network communities and have seen a lot of visualizations showing my connections to others, but let’s face it, I already know my network and the topics I share with others. Accordingly, my network nourishes itself bringing people together around any topic inside or outside the inner circle, in a way only human intention can. Do I really expect that a computer’s analysis will provide unique and unexpected connections?

I am not a complete naysayer, there is merit in this work, but the current state of eye candy provides little value, showing people what they already know. If, by chance, users discover something new, the value of the system diminishes, because there is even less to unveil – a tough proposition for social systems to overcome.