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Code and analysis written for "Measuring Musical Sampling Impact Through Network Analysis". Intended to determine patterns of artistic and genre influence in the music industry.
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Analysis
Tran_final_paper
README.md
basicGraph.py
communityGraph.py
egoGraph.py
timePeriodGraph.py

README.md

Measuring Musical Sampling Impact Through Network Analysis

Summary poster of findings

What is musical sampling and why is it interesting?

Musical sampling influence has only recently been studied through network structures through the basic analysis of artist-artist sampling relationships. In this piece of research, we integrate the use of additional properties of music sampling (such as genre, time period, and audio element sampled) to investigate patterns of influence in the musical community at large.

How do we investigate musical sampling using graph theory?

Using the WhoSampled dataset and NetworkX for Python, we investigate statistical metrics such as the most-sampled artists songs as well as the trend for musical sampling over time. We also take a more nuanced look at "influence" by providing a variety of graph centrality measurements for determining the influence of a node (representing an artist) on other nodes.

What were the results of the research?

This analysis resulted in a greater understanding of musical influence certain artists and genres had over other heavily-sampling artists and genres over time. The most influential genre was found to be Funk/Soul/Disco while the most influential artist of all time was James Brown. More specific influencers from different time periods were also found. We conclude with possible future research that can be applied to this network analysis of musical sampling.

Where can I find the most pivotal details?

The full paper can be found here.

The slide deck for a quick summary can be found here.

The high-resolution poster can be found here.

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