This document summarizes a network analysis of popular musicians' similarity that aimed to understand and recommend artists. It analyzed data on the top 1000 hottest artists and their similar artists scraped from Echo Nest. A network visualization showed artists' similarity connections follow a power-law distribution. Pagerank centrality was calculated for different alphas and 74 sub-communities were identified. The analysis also developed an artist recommendation system based on an individual's taste profile and recommending artists through the shortest path.

1. A NETWORK ANALYSIS of
POP MUSICIANS’ SIMILARITY
Team Member:
Cindy Liu, Yuxiang Zhang
December 14th, 2015

2. THE GOALS!
TO
UNDERSTAND
TO RECOMMEND

3. OUR DATA
SOURCE
RULE
RESULT
Top 1000 hottest artists and
their similar artists
1000
Artists
1000 Artists
* Scraped from Echo Nest on December 12, 2015

4. NETWORK VISUALIZATION

5. PAGERANK CENTRALITY
α = 0.95 0.7 0.5 0.3 0.1

6. ARTISTS’ SIMILARITY CONNECTIONS
FOLLOWS A POWER-LAW DISTRIBUTION

7. SEVENTY FOUR SUB-COMMUNITIES

8. ARTIST RECOMMENDATION

9. ARTIST RECOMMENDATION
TASTE
PROFILE
SHORTEST
PATH
RECOMMEND
ARTISTS

10. Thank you!