1 BBL Drizzy Context
Inspired by Metro Boomin’s contributions to the Kendrick Lamar vs. Drake diss tracks, I created a network analysis of these interactions.
Almost 20 years ago, I sat in Mark Granovetter’s Social Network Class PhD course at Stanford with Jason Davis, discussing how to conceptualize and model negative ties. Jason speculated that diss tracks could be ideal for modeling these negative ties. Today, I admit that he might be marginally smarter than me. So, I present this Diss-Network, including the BBL Drizzy Beef.
I simply sourced the names and connections for wikipedia
Spoiler: Tupac and Drake are tied in these data for spewing the most diss-tracks.
2 Diss Track Network Graph
Below are all the artists listed on Wikipedia who have made diss tracks and their targets, forming a directed network. You can zoom in and move the image to see connections. You can also select artists from the dropdown menus to filter the view by incoming diss tracks. Note: You can resize the graphs and relocate individual nodes.
3 Take a look at the Diss-Track network data.
3.1 Network Size
Network Size refers to the number of nodes in the
network, which, in this case, is the number of artists in this dataset
who have ever mentioned another artist in a diss track.
## The network size of Diss-Graph is 2883.2 Density
Density refers to the proportion of possible ties that
actually exist in the network. It is calculated by dividing the number
of observed connections by the total number of possible connections. A
higher density indicates more artists are dissing each other in their
songs.
## The density of Diss graph is 0.0037020913.3 Diameter
Diameter refers to the longest shortest path between any
two nodes in the network, measuring the maximum distance between any two
nodes.
## The diameter of Diss graph is 73.4 Average Path Length
Average path length is the average of all shortest path
lengths between any two nodes in the network. A smaller average path
length indicates that nodes are closely connected, making it easier for
information to flow.
## The Average Path Length of Diss graph is 2.4390244 Network Centrality Statistics for the Diss-Track Network
4.1 Degree centrality
Degree centrality measures the number of ties a node has
in an undirected network. In a directed network,
In-degree centrality centrality measures the number of
incoming ties, while Out-degree centrality measures the
number of outgoing ties.
[1] "Diss Graph Indegree Centrality Distribution" Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000000 0.003484 0.003690 0.003484 0.048780 [1] "Diss Graph Outdegree Centrality Distribution" Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000000 0.000000 0.003690 0.003484 0.087108 4.2 Betweenness centrality
Betweenness centrality measures the extent to which a
node lies on the shortest path between other pairs of nodes. Individuals
with high betweenness centrality are “between” more disputes in the
network.
[1] "Diss Graph Betweenness Centrality Distribution" Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000000 0.0000000 0.0000000 0.0000574 0.0000000 0.0030031 4.3 Eigenvector centrality
Eigenvector centrality measures a node’s influence in
the network based on its connections to other well-connected nodes. High
eigenvector centrality indicates greater influence and status. In this
case, it would be the opposite and you might be the most
disrespected.
[1] "Diss Graph Eigenvector Centrality Distribution" Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0 0 0 4.4 Closeness centrality
Closeness centrality measures how close a node is to all
other nodes in the network, based on the shortest path lengths. Nodes
with high closeness centrality can access information and resources more
easily.
[1] "Diss Graph Closeness Centrality Distribution" Min. 1st Qu. Median Mean 3rd Qu. Max.
0.1104 0.1606 0.4533 0.5072 1.0000 1.0000 5 Artist Diss-Network Measures
In the tables below, you can filter by your favorite artist’s name to find their corresponding network values.