Social Network Analysis of the Professional Community Interaction—Movie Industry Case

• Published in: Data Analytics and Management in Data Intensive Domains Vol. 1620; pp. 36 - 50
• Main Authors: Karpov, Ilia, Marakulin, Roman
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: IMDb; Machine learning; Movie rating; Node2Vec; Prediction; Social network analysis
• ISBN: 9783031122842; 3031122844
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-031-12285-9_3

With the rise of the competition in the movie production market, because of new players such as Netflix, Hulu, HBO Max, and Amazon Prime, whose primary goal is producing a large amount of exclusive content in order to gain a competitive advantage, it is extremely important to minimize the number of unsuccessful titles. This paper focuses on new approaches to predict film success, based on the movie industry community structure, and highlights the role of the casting director in movie success. Based on publicly available data we create an “actor”-“casting director”-“talent agent” - “director” communication graph and show that usage of additional knowledge leads to better movie rating prediction.