ODSC: Analyzing Complex Networks Part 2

This is part two of a brief series sharing components of my presentation titled Analyzing Complex Networks Using Open Source Software at ODSC East in Boston on May 21st. The first post looked at a few examples from a Boston Red Sox players network, while this one examines a Miles Davis album and musician network. I’ll share a few examples of network analysis within the context of the Miles Davis graph.

The Miles Davis network could be described as a tripartite network, or one with three layers. Miles is at the center, and connects to each of nearly 50 recordings. Other musicians then connect to the respective recording(s) they played on, but not to one another. This approach provides a very clear look at musical phases in the career of the legendary trumpeter, without the graph being clouded by excessive detail. Here’s a view of the final network, after which we’ll look at some components of the graph.

miles_1

We see some interesting patterns in the graph, specifically in viewing the pink circles, which represent individual albums. Musicians playing on a recording can be seen adjacent to that recording, except in the case of musicians present on multiple albums. We would expect them to be positioned relative to all of the recordings they played on. A quick visual scan leads to five distinct clusters, as seen in the next screenshot.

miles_2

Now that we have identified these clusters, it would be helpful to understand their meaning and relevance to Miles career. Using the graph in interactive fashion, we can learn more about the recordings and musicians, and begin to formulate some insights. These can be confirmed by referring to album links on the web or in Wikipedia, which give context to what we are viewing. Based on these steps, here is a quick overview of the five clusters.

miles_3

A final step might be to add some verbiage using PowerPoint or Inkscape, which I’ve done below in very minimalist fashion. We could also add this to a web version using CSS attributes to position the text, although this could get tricky as we pan and zoom on the graph. We might be better off using some sort of stylized marker (color or shape) to communicate some of this information.

miles_4

There is much more that could be done, but I hope this brief example shed some light on the usefulness of network graphs, especially from a pure visual perspective.

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ODSC: Analyzing Complex Networks Using Open Source Software

I’ll be presenting at the 2016 ODSC East event in Boston May 20-22. ODSC stands for Open Data Science Conference, where the focus is on using open data or open source tools to do clever things in the information space. The topic of my presentation is Analyzing Complex Networks Using Open Source Software, where I’ll talk through several example networks built using Gephi and Sigma.js.

While the slides are not all prepared at this stage, I’ll share a few bits that will wind up in the talk. My goal is to convey to the audience how networks can be used to statistically and visually understand complex information. After providing an overview of network analysis (at a very high level), I’ll be sharing slides from three very different networks – a Miles Davis album network (created in 2014 and rebuilt in 2016), a Boston Red Sox player network (also built in 2014), and a brand new example using data from the amazing GDELT Project.

Here’s a glimpse into what I’ll be sharing, starting with the Red Sox examples, where we examine the networks of three well known players from the last 100 years. First, Ted Williams network:

odsc_williams

Followed by Carl Yastrzemski:

odsc_yaz

Now Jason Varitek, longtime catcher and captain for two World Series championship teams:

odsc_varitek

In talking through each of these networks, I will attempt to highlight some differences in their respective structures based on the era in which each player spent time with the Red Sox. For example, there are many more connections in the Varitek network compared to Williams and Yaz, despite a shorter duration with the team. Why would this be the case? Perhaps spending time in the era of higher salaries, larger pitching staffs, and the evolution of free agency might go a long way towards explaining why Jason Varitek crossed paths with far more players than did his earlier predecessors.

Stay tuned for additional posts featuring the Miles Davis and GDELT networks.

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