Long, long ago, I studied music. In fact, when I finished high school, music was all I wanted to study. To be sure, I didn’t just want to study it: I wanted to compose it as well. 1 But I soon discovered that music theory was something worthy of study in itself, quite apart from the grounding it provided for composition. Music theory, especially the analysis of harmonies and harmonic progressions, provided a way to pop the hood on a piece of music (or even a whole genre) and learn what makes it tick. As if that weren’t exciting enough, I sensed that there were more profound truths waiting to be teased out of these harmonic structures. For if they offered clues about what makes music tick, then surely they said something about what makes us tick as well.
I never did pursue my vision of a grand unified theory of tonal harmony and psychoacoustics. I soon found that there were also other things worth studying, many of which came with the bonus incentive of career prospects. One thing led to another, and for better or worse, I ended up working for the government. And not as a music theorist. But to this day, I can’t help hearing a piece of music and thinking about what makes it tick. The theorist within me is always plugging away, even while the rest of me is just enjoying the tune.
Unsurprisingly then, when I started playing with network graphs about 18 months ago, among the first things I asked myself is what application they might have for music theory. The beauty of network graphs is that they can be used to represent just about anything. Any system or community of inter-related parts can be turned into a network of nodes and connections. So far on this blog I’ve used network graphs to explore the linkages among websites related to coal seam gas, and to identify clusters of documents containing duplicated text. On my other blog, I used network graphs to see how the names of different people and places featured across a collection of my posts.
In this post, I will use network graphs to visualise the relationships among chords within a piece of music. You could examine melodies in much the same way, by breaking them down to their individual notes and tracking which notes pair up and cluster together most often. But I suspect that there is more to be gained from visualising the harmonic relationships. Continue reading Adventures in harmonic space