Worksheet: Tracking the evolution of symmetric ties on Google Buzz

At the end of May, I introduced a tool for monitoring the up-to-the-minute status of my students' bootstrapping efforts on buzz (the code is available as open source software here). Today, I'm introducing another tool for tracking the evolution of symmetric ties in Buzz networks. Symmetric ties, where two people mutually follow each other's updates on the network, are important because the more symmetric ties a participant has, the more possibilities they will have for conversation and hence the more useful they will find the network.

You can see an excerpt of the tool's output in the image above. It's basically a web page where participants in the Web Marketing Practicum see three columns beside their mini profiles:

The number of symmetric (mutual) ties they had as of the analysis date, along with links to all of those people's profiles.
The number of symmetric ties that they added since a reference date, again with links to the actual profiles.
The number of symmetric ties that they subtracted since a reference date, again with profile links.

Using this information, participants can see who is entering and leaving each other's mutual ties networks. Perhaps more importantly, discussion can ensue about the type of network each participant is trying to grow and whether the additions and subtractions that have occurred make sense in that context.

A few quick remarks:

With one exception, every student has at least doubled the size of their symmetric ties network since May 31, 2010 when I did my first analysis.
In the two weeks covered by the pictured analysis, students typically increased the size of their symmetric ties network by over 20%.

How the tool might be useful

I was interested to read Adewale Oshineye's Buzz post where he, in effect, wondered why people would be concerned with symmetric ties on Buzz. So, it seems useful to list a few reasons why a symmetric ties tracking tool might be useful:

It's hard to imagine people exchanging information on a sustained basis unless they are mutually following each other (i.e., have symmetric ties). Knowing that your symmetric ties network is growing is an indicator that the network is a useful source of exchange.
People, being people, tend to seek reciprocation in their social relationships. They find non-reciprocating (asymmetric) ties less appealing and will deemphasize networks where such ties abound. Knowing who is in your symmetric ties network and how it is changing over time is a useful indicator of the kind of value you're getting out of it. Is the network you're growing on the platform a colleagues network, a family network, a friends network, or what?
In general, it pays to have visual indicators of effectiveness. Buzz currently lets you know how many people you are following and how many are following you. However, there is strong evidence that high follower counts do not translate into reach. On the face of it, symmetric tie counts might be a better indicator (but see limitations below).

Tool limitations

The analysis is purely descriptive. It only shows how one aspect of the networks has evolved. It doesn't shed any light on why or how that evolution occurred.
The way the tool is set up, it suggests that gaining more and more symmetric ties is a good thing. I believe that bias to be useful when you're starting out. However, at some point, it doesn't make sense to try to increase your symmetric ties beyond what you can effectively track.
The tool places no weight on the quality of the tie. Is this someone it makes sense for you to be tied to?

Future directions

If you step back, you realize that Buzz is one instantiation of the next phase of personal publishing. A number of thoughts arise:

I'm interested in knowing more about the connection structure in these networks. How are people down through the friends of friends structures connected?
I'm interested in expanding the analysis beyond the current core group of people. If I go to friends of friends, I start to have enough data to make computing individual page rank interesting.
I wonder to what it extent it would pay to make friend recommendations from this data.