DETECTING COLLABORATION PROFILES IN SUCCESS-BASEd MUSIC GENRE NETWORKS Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, Mirella M. Moro
PROFILES Solid, Regular, Bridge and Emerging highlightshighlights
COLLABORATION in general, genre collaborations are LOCAL MATTERS increasing local genres play a key role on determining hit songs
introductionintroduction collaborations are more popular than ever
1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 2 95 96 96 97 97 97 98 98 01 99 99 99 00 00 01 01 01 8 2 6 0 4 8 2 6 4 0 4 8 2 6 0 4 8 objectiveobjective We aim to unveil the dynamics of cross-genre connections and collaboration profiles in success-based networks
Does the regional aspect impact on popular genres Rq1Rq1 and their hit songs?
How has genre collaboration evolved over the past Rq2Rq2 few years?
Which are the potentially intrinsic factors and Rq3Rq3 indicators that influence the collaboration success?
METHODOLOGyMETHODOLOGy
build MODEL DETECT Proper dataset Success-based Collaboration Genre Network Profiles
Music GENRE dataset (mgd)
Spotify API Data Weekly Song Charts Global and Regional songs, artists, genres from 2017 to 2019 top music markets
mgd enhanced genre collaboration data
1,330 Charts 13,380 Hit Songs 3,612 Artists 896 Genres
Does the regional aspect impact on popular genres and Rq1 their hit songs? most popular music genres in each considered market (2017 - 2019)
Global Australia Brazil Canada GermLany France UK Japan USA
GENRE COLLABORATION NETWORK
Hit Songs nodes are divided into three sets: artists genres, artists, and hit songs i.e., the minimum elements to evaluate genres success Tripartite Graph 1
collaborative hit songs are sung by two or more artists, 1 1 regardless of their 1 participation (e.g., a typical feat. or a duet)
Artist Network 2 Intermediate Step
1 one-mode network in which nodes are 1 2 exclusively genres, where edge weight is the 2 number of hit songs by 1 artists from both genres Genre Network 3 self-loops are allowed and represent intra-genre collaborations
Rq2 How has genre collaboration evolved over the past few years? Network characterization
709
564 583
257 247 237
72 79 89 16 15 16 2017 20L18 2019
GROUPGROUP 11
9.21% 12.59% 4.72% # genres avg. degree avg. clustering coefficient
GGRROOUUPP 22
3.17% 22.29% 20.15% # genres avg. degree avg. clustering coefficient
GGRROOUUPP 33
6.76% 3.64% 2.12% # genres avg. degree avg. clustering coefficient
GENRE collaborations profiles
GENRE colExploratorylaborat factor analysis
Preferential Attatchment F1 Attractiveness Common Neighbors
Resource Allocation F2 Affinity Weight
Neighborhood Overlap F3 Influence Edge Betweenness
cluster analysis dbscan Clustering result USUSAA nenetworktwork (2019)(2019)
Cluster 0 Cluster 1 Cluster 2 Cluster 3
7.5 rock - new wave
hip hop - rap other - classical 5.0 rock - punk
rap - pop rap rap - trap pop - folk rock rap - rap rock - experimental pop - lounge 2.5 pop - pop lounge - swing trap - pop pop - rock dance pop - rock
Dim2 (17.7%) pop - other pop - r&b classic rock - experimental rap - r&b pop - reggaeton hip hop - dance pop dance pop - brazilian funk 0.0 pop - house indie rock - rap rock dance pop - dance pop latin - reggaeton new wave - punk latin - latin soul - soul k-pop - k-pop reggaeton - reggaeton moombahton - moombahton -8 -4 0 4 Dim1 (66.1%) collaboration profiling Collaboration profiles for all markets (2019)
Preferential Common NeighborhooRdesourceEdge Resource Weight Attatchment Neighbors Overlap AllocatioBne tweenness Allocation
Attractiveness Affinity Influence
Which are the potentially intrinsic factors and indicators that Rq3 influence the collaboration success? Median Number of Streams
2017 20L18 2019
PROFILE 0 ssoolliidd
2017 20L18 2019
high Attractiveness and Affinity POP medium Influence successful genre collaborations HIP-HOP inter-genre collaborations
PROFILE 1 RREGEGULULAAR
2017 20L18 2019
medium values
POP successful genre collaborations SOUL inter-genre collaborations
PROFILE 2 bribriddggee
2017 20L18 2019
high Influence
low Attractiveness and Affinity GOSPEL
less successful RAP ONLY INTER-GENRE COLLABORATIONS
PROFILE 3 EMEMERGERGIINGNG
2017 20L18 2019
medium Attractiveness and Affinity
k-pop low Influence k-pop less successful intra-genre collaborations
base material further r research tasks, e.g., prediction and recommendation conclusionconclusion
music industry maximize expected success by artists properly investing in potential profit by identifying the collaborations most suitable partnerships contactcontact usus
gabriel p. oliveira mariana o. silva [email protected] [email protected] OUR PROJECT
Danilo b. Seufitelli anisio lacerda mirella m. moro [email protected] [email protected] [email protected]