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The modern age of digital music access has increased the availability of data about music consumption and creation, facilitating the large-scale analysis of the complex networks that connect musical works and artists. Data about user streaming behaviour and the musical collaboration networks are particularly important with new data-driven recommendation systems. Here, we present a new collaboration network of artists from the online music streaming service Spotify and demonstrate a critical change in the eigenvector centrality of artists, as low popularity artists are removed. This critical change in centrality, from a central core of classical artists to a core of rap artists, demonstrates deeper structural properties of the network. Both the popularity and degree of collaborators play an important role in the centrality of these groups. Rap artists have dense collaborations with other popular artists whereas classical artists are diversely connected to a large number of low and medium popularity artists throughout the graph through renditions and compilations. A Social Group Centrality model is presented to simulate this critical transition behaviour, and switching between dominant eigenvectors is observed. By contrasting a group of high-degree diversely connected community leaders to a group of celebrities which only connect to high popularity nodes, this model presents a novel investigation into the effect of popularity bias on how centrality and importance are measured. [ABSTRACT FROM AUTHOR]

The number of characters in a movie is an important feature. However, it is non-trivial to measure directly, for example naive metrics such as the number of credited characters vary wildly. Here, we show that a metric based on the notion of ecological diversity as expressed through a Shannon-entropy based metric can characterise the number of characters in a movie, and is useful in taxonomic classification. We also show how the metric can be generalised using Jensen-Shannon divergence to provide a measure of the similarity of characters appearing in different movies, for instance of use in recommendation systems, e.g., Netflix. We apply our measures to the Marvel Cinematic Universe (MCU), and show what they teach us about this highly successful franchise of movies. In particular, these measures provide a useful predictor of success for films in the MCU, as well as a natural means to understand the relationships between the stories in the overall film arc. [ABSTRACT FROM AUTHOR]

Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior. [ABSTRACT FROM AUTHOR]