Deciphering versatility and cooperation in multilayer social networks.
Year: 2015
Type: preprint
Abstract: Despite large scale availability of social data, our understanding of the basic laws governing human behaviour remains limited, owing to the lack of a proper framework which can capture the interplay of various interdependent factors affecting social interactions. In the recent years, multilayer networks has increasingly been realized to provide an efficient framework for understanding the intricacies of complex real world systems. The present study encompasses the multilayer network analysis of Bollywood, the largest film industry of the world, comprising of a massive time-varying social data. Making around 1500 films annually, Bollywood has emerged as a globally recognized and appreciated platform for cultural exchange. This film industry acts as a mirror of the society and the rapidly changing nature of the society is reflected in the depictions of films. This renders this model system to provide a ripe platform to understand social behaviour by analyzing the patterns of evolution and the success of individuals in the society. Similarity in the degree distribution across the individual layers validates our basis of multilayering. While the degree-degree correlations of the whole networks reveal that in general nodes do not exhibit any particular preference for pairing up, multilayer framework indicates the gradual restoration of cooperation in the recent dataset. Working in more number of genres comes up as an intrinsic property of lead nodes. Further, versatility in pairs and triads has been shown to demonstrate its impact on the success of lead nodes. While repeated cooperation in pairs of dissimilar types of nodes has been shown to yield success to the lead nodes, triads of similar types of nodes turn out to be more successful. Weak ties analysis emphasize on the importance of every type of node in the society.
Source: arXiv (Cornell University)
Authors: Camellia Sarkar, Alok Yadav, Sarika Jalan
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1692028935