2013/05/08 by Lazaros K. Gallos, Fabricio Q. Potiguar, F. Q. Potiguar +4
Computer Science · Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Biology #Combinatorics #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Complex network #Computer network #Computer science #Focus (optics) #Fractal #Fractal analysis #Fractal dimension #Mathematics #Modular design #Modularity (biology) #Network analysis #Network science #Network structure #Network topology #Opinion Dynamics and Social Influence #Physics #Social media #Social network (sociolinguistics) #Theoretical computer science #Topology (electrical circuits) #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1371/journal.pone.0066443
published as PLOS One, volume 8, issue 6, e66443 (2013) · 12 pages, 9 figures, accepted for publication in PLOS ONE
arxiv created 2013/05/08 · openalex publication_date 2013/06/24 · arxiv updated 2014/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We study a subset of the movie collaboration network, http://www.imdb.com, where only adult movies are included. We show that there are many benefits in using such a network, which can serve as a prototype for studying social interactions. We find that the strength of links, i.e., how many times two actors have collaborated with each other, is an important factor that can significantly influence the network topology. We see that when we link all actors in the same movie with each other, the network becomes small-world, lacking a proper modular structure. On the other hand, by imposing a threshold on the minimum number of links two actors should have to be in our studied subset, the network topology becomes naturally fractal. This occurs due to a large number of meaningless links, namely, links connecting actors that did not actually interact. We focus our analysis on the fractal and modular properties of this resulting network, and show that the renormalization group analysis can characterize the self-similar structure of these networks.