One of the many skill development opportunities available at 8th Light is a "mastery cohort", where an acknowledged "master" with years of experience in the software industry is invited to come give a day-long workshop for 8th Light crafters. Last Friday, Bobby Norton came to give a mastery cohort on data science and network analysis, and apprentices were invited to participate. I was pretty enthusiastic to sign up because I've done a lot of dabbling while studying genomics and systems biology; a lot of my research interests as a scientist revolved around the emergent properties of complex systems and understanding the behavior of gene regulation and metabolic networks. I haven't studied graph theory at a rigorous level, other than reading some papers by Barabási and doing a course project on "subgraph" patterns (incidentally, also the project where I took the opportunity to first teach myself Python!) way back in second year of graduate school, but the subject continues to fascinate me.

The Big Idea of the cohort was to apply the ideas from network analysis and graph theory to software, which is in and of itself a complex system, and to see if the insights that emerge could be used to identify the characteristics of well-designed software and (conversely) diagnose problems in code more quickly. It's one of those ideas that as soon as you hear it, you wonder why it hasn't been done already! We looked at some tools for representing and visualizing graphs and networks as well as Bobby Norton's own library written in Clojure for analyzing dependencies and functions in Clojure codebases.

vis.js: Javascript-based visualization library that can be used to draw graphs and network diagrams

Gephi: A visualization GUI tool that is fairly popular and can be used for exploring network data.

yEd: Similar to Gephi, with a slicker interface. Main disadvantage is that there is no support for weighted edges.

GraphML: XML-based standard for representing graph data. File format should be supported by most GUIs.

Polinode: Cloud-based platform for network analysis.

iGraph: Package for network analysis. Available for R and Python.

edgewise: Clojure library for network analysis.

lein-topology: Generates graph data for a given Clojure library, can be used in conjunction with edgewise above to analyze the software network structure.

Also, I don't know any Clojure yet, but since so much of the day was spent with Clojure-based tools, I did collect a few links for learning Clojure:

4Clojure: A koan/kata-like site for learning Clojure through exercises.

Gorilla: A notebook-type REPL for Clojure, kind of like Jupyter and apparently better than the actual Clojure kernel for Jupyter (CloJupyter), which has a few bugs.

Some ideas I had for studies that could be done investigating the network analysis of software:

• Get network representations of many software libraries in a given language, and have some independent metric(s) for either performance or design quality. Show statistically significant correlations between network properties and such metrics.

• Time series showing how the network architecture of software evolves while moving from a "small-scale" to "large-scale" project.

• Do different types of languages lend themselves to different types of network topologies? (E.g. functional vs object-oriented vs procedural.)

• Do different "subgraphs" correlate with well-known "design patterns" outlined in best practices for software design?

I wonder if any of these have already been done in the theoretical computer science field. Worth searching on arXiv, one of these days.