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Networks and Artistic Status Orders in Cultural Fields:The Evolution of Hollywood Filmmaking
No full textHow do status orders emerge in cultural fields? Our study sheds new light on this question by investigating the interplay of networks and status among Hollywood filmmakers from 1920 to 2000. Information on artistic references and collaborations of more than 9,500 filmmakers retrieved from the Internet Movie Database (IMDb) allows us to examine long-term changes in the social organization of this cultural field. Our findings suggest that the distribution of social recognition—measured by filmmakers’ prominence in collaborative ties and artistic references—became more stratified as the field grew. Furthermore, collaborations increasingly exhibited segregation according to filmmakers’ artistic status during the New Hollywood era (1960–1980). This period was characterized by the rising prominence of a new generation of filmmakers who established film as an art form in the U.S. This article shows that contextual characteristics, such as a field's size and institutional environment, can foster or impede stratification and segregation in collaborative networks among cultural producers
Initialisation and network effects in decentralised federated learning
No full textFully decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices while keeping the training data localised on each node. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. Our research highlights that the effectiveness of decentralised federated learning is significantly influenced by the network topology of connected devices and the initial conditions of the learning models. We propose a strategy for uncoordinated initialisation of the artificial neural networks based on the distribution of eigenvector centralities of the underlying communication network, leading to a radically improved training efficiency. Additionally, our study explores the scaling behaviour and the choice of environmental parameters under our proposed initialisation strategy. This work paves the way for more efficient and scalable artificial neural network training in a distributed and uncoordinated environment, offering a deeper understanding of the intertwining roles of network structure and learning dynamics
