hollywood_music — Hollywood film music (1964-1976)
- Description
A bipartite network of the collaborations between composers of film scores and producers in Hollywood, compiled by R. R. Faulkner. It includes the 62 producers who produced at least five films in Hollywood between 1964 and 1976, and the 40 composers who created the soundtrack of their films. An edge connects a composer to a producer if the composer created the music for at least one film by the producer in that period, and is weighted by the number of such films. One of the composers (R. Lasalle) has no edges in the source data. The data were distributed on the Pajek datasets page of the book Exploratory Social Network Analysis with Pajek (de Nooy, Mrvar and Batagelj), from which this version was obtained.1
The network has the following properties:
- Nodes:
name, the name of the producer (or production company) or composer;is_producer, whether the node is a producer (otherwise a composer);top_composer, whether the composer is one of the five top composers, each of whom earned 1.5% or more of the total income of Hollywood film composers in the 1960s and 1970s. - Edges:
weight, the number of films by the producer for which the composer created the music.
- Nodes:
- Tags
- Social Collaboration Weighted Metadata
- Citation
-
- R. R. Faulkner, "Music on Demand: Composers and Careers in the Hollywood Film Industry." Transaction Books, New Brunswick (1983) web.archive.org
- W. de Nooy, A. Mrvar, V. Batagelj, "Exploratory Social Network Analysis with Pajek." Cambridge University Press (2005) 10.1017/CBO9780511806452 Sci-Hub
- Upstream URL OK
- https://web.archive.org/web/20040706122343/http://vlado.fmf.uni-lj.si/pub/networks/data/esna/movies.htm
- Networks
-
Tip: hover over a column header for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties