Tag: network graph

Graph Style Updates

In an effort to make all graphs stylistically consistent with the look & feel of this site, I have managed to change the base text for the graphs to the stylish Rokkitt font, as seen in the title of the image below. The Rokkitt font has a unique style that seems quite appropriate for jazz networks.

You may have also noticed the large bubble in the network graph, which represents the lens capability provided for scanning the graph. I have recently updated the lens settings to enable easier browsing of the network. You can activate the lens by clicking on the magnifying glass in the lower left of the screen. While the lens is great for browsing the graph, you may find it easier to make individual selections with the lens deactivated; simply click the icon a second time to toggle the lens to the off setting.

As a reminder, selecting any graph node will display all linked entities in the graph, as seen below. All links in the left panel are now also featuring the Rokkitt font.

These style modifications will be applied to all future jazz graphs, as I work to create informative, easy to use, stylish network graphs. Thanks for reading!

ECM Label Network

One of the great strengths of the MusicBrainz database is that we can build networks using not only artists, but also record labels, or even individual songs. In this post, we’re going to explore an example (per a reader suggestion) using the ECM label and its variants. ECM is known for producing high quality recordings from an array of both jazz and classical artists. Keith Jarrett, Charles Lloyd, and Gary Burton are among some of the better known artists with multiple ECM recordings.

In contrast to our Miles Davis network, where the focus was on the artist, we now wish to see the labels (ECM) as hubs within the network. We’ll take a similar approach to constructing the node and edges files, although we now are going to create a multi-modal structure with 4 layers: Label –> Artist –> Release –> Songs. Using PostgreSQL, we can pull this data quite easily from the MusicBrainz database. Let’s start with the nodes logic:

SELECT a.*
FROM
((SELECT CONCAT(l.name, ‘ (Label)’) AS id, l.name AS name, l.name AS label, ‘Label’ AS type, 30 AS size
FROM label l
WHERE l.id IN(46800,1884,106711,123517))

UNION ALL

(SELECT CONCAT(ac.name, ‘ (Artist)’) AS id, ac.name AS name, ac.name AS label, ‘Artist’ AS type, 10 AS size

FROM public.release r
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.medium m
ON r.id = m.release
INNER JOIN public.medium_format mf
ON m.format = mf.id
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id

WHERE l.id IN(46800,1884,106711,123517))

UNION ALL

(SELECT CONCAT(r.name, ‘ (Release)’) AS id, r.name AS name, r.name AS label, ‘Release’ AS type, COUNT(DISTINCT rl.release) AS size

FROM public.release r
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id

WHERE l.id IN(46800,1884,106711,123517)
GROUP BY r.name
)

UNION ALL

(SELECT CONCAT(ta.name, ‘ (Song)’) AS id, ta.name AS name, ta.name AS label, ‘Song’ AS type, COUNT(DISTINCT rl.release)
AS Size
FROM public.release r
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.medium m
ON r.id = m.release
INNER JOIN public.medium_format mf
ON m.format = mf.id
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.label l
ON rl.label = l.id
INNER JOIN public.track t
ON m.id = t.medium
INNER JOIN public.track_aggregate ta
ON t.name = ta.name

WHERE l.id IN(46800,1884,106711,123517)

GROUP BY ta.name
)) a

The four sections of code are united by one common attribute – the four record label identifiers associated with ECM. Section 1 creates nodes for the record labels, section 2 the recording artists, section 3 the releases, and section 4 the songs on each release. This should give us a very interesting network, although it will not have the same level of cross-pollination as the earlier Miles Davis network, as the songs are being associated with specific releases.

Creating the edges is quite similar, albeit requiring just three sections of code:

SELECT a.*
FROM
((SELECT CONCAT(l.name, ‘ (Label)’) AS source, CONCAT(ac.name, ‘ (Artist)’) AS Target, ‘Label’ AS source_type, ‘Artist’ AS target_type
FROM public.label l
INNER JOIN public.release_label rl
ON l.id = rl.label
INNER JOIN public.release r
ON rl.release = r.id
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id

WHERE l.id IN(46800,1884,106711,123517))

UNION ALL

(SELECT CONCAT(ac.name, ‘ (Artist)’) AS Source, CONCAT(r.name, ‘ (Release)’) AS Target, ‘Artist’ AS source_type, ‘Release’ AS target_type
FROM public.release r
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.label l
ON l.id = rl.label

WHERE l.id IN(46800,1884,106711,123517))

UNION ALL

(SELECT CONCAT(r.name, ‘ (Release)’) AS Source, CONCAT(ta.name, ‘ (Song)’) AS Target, ‘Release’ AS source_type, ‘Song’ AS target_type
FROM public.release r
INNER JOIN public.release_label rl
ON r.id = rl.release
INNER JOIN public.artist_credit ac
ON r.artist_credit = ac.id
INNER JOIN public.label l
ON l.id = rl.label
INNER JOIN public.medium m
ON r.id = m.release
INNER JOIN public.track t
ON m.id = t.medium
INNER JOIN public.track_aggregate ta
ON t.name = ta.name

WHERE l.id IN(46800,1884,106711,123517))
) a
GROUP BY a.source, a.target, a.source_type, a.target_type

Here we are simply connecting labels to artists, artists to releases, and releases to songs. Both the node and edge results are saved to .csv files for use in Gephi.

Once the data was in Gephi, I spent parts of a few days testing layouts, spacing, colors, sizing, and so on, before settling for the moment on using the popular Force Atlas 2 algorithm. I find it useful to start the process using the rapid (and less precise) OpenOrd algorithm whenever working with a fairly complex or large dataset. Then, once the basic network structure is revealed, we can move on to Yifan Hu, Force Atlas, or any of the more precise methods.

For the (for now) final version, I elected to size the nodes based on the number of outbound degrees, which will place more visual emphasis on the record labels and releases, respectively. Artists with multiple releases will also be represented by slightly larger nodes. So here are two versions, the first in Gephi:

The second version is from the web, after tweaking settings in sigma.js:

To interact with the network, click here. Thanks for reading!

Visualizing Miles, Part 2

One of my favorite aspects of working with web projects is the ability to use CSS to customize a page. This is especially the case when working with sigma.js for the deployment of network graphs. CSS makes it easy to quickly test and change colors, modify elements, and experiment with different fonts. This is all important when I’m seeking a particular look and feel for a visualization. Which leads into this updated take on my recently created Miles Davis network graph, wherein the node colors, edge widths, and font sizes have all been modified, for the better, I believe.

In place of the nearly black background is a deep, rich blue, as well as new node colors and a more readable font color for the sidebar. Here are the before and after views:

 

And the updated look: