One of the challenges of writing an elder blog is that by definition the archives are extensive, and of very mixed quality. At some point, all formally imposed structure — categories, tags, series, “best of year” or “most popular” lists — buckle under the sheer weight of content. Once you’re past a few hundred posts, with reasonably dense internal back-linking, your only hope for recovering some sort of structure from what is essentially a little walled-garden artisanal web is algorithms. Thanks to John Backus, I have an algorithmic lens on the unkempt wilderness of ribbonfarm for you today.
John mined the archives to compute the internal linking structure, which I then massaged further into an internal page rank for the archives. Here’s a little video of John playing with a graph visualization tool.
And here’s the spreadsheet with the mined data. Feel free to make a copy and play around with the data and my PageRank-esque formula, which generates this view of the archives:
The “Adjusted Page Rank” here is a function of three variables:
- The number of posts linking to a post. A good post should inspire the author, and hopefully other contributors, to cite it in future posts.
- The age of the post. If a post doesn’t accumulate backlinks, it sinks into obscurity. About half the posts in our archives have no backlinks.
- The “weight” of the author. Contributors who have written more are weighted less, so Sarah and I have the two lowest weights, at 1.0303 and 1.0037 respectively.
Note that external inbound links are specifically not included in this ranking. This is a purely internal measure. If you want the formulas:
Author_weight = 1+1/(num_posts)
Adjusted Page Rank = Author_weight*num_links/age
Where num_posts is the number of posts with at least 1 backlink.
Obviously, there’s room for enhancements here, but it’s a start. Thanks John!