Balance of thrones: a network study on 'Game of Thrones'

TV dramas constitute an important part of the entertainment industry, with popular shows attracting millions of viewers and resulting in significant revenues. Finding a way to explore formally the social dynamics underpinning these show has therefore important implications, as it would allow us not only to understand which features are most likely to be associated with the popularity of a show, but also to explore the extent to which such fictional world have social interactions comparable with the real world. To begin tackling this question, we employed network analysis to systematically and quantitatively explore how the interactions between noble houses of the fantasy drama TV series Game of Thrones change as the show progresses. Our analysis discloses the invisible threads that connected different houses and shows how tension across the houses, as measure via structural balance, changes over time. To boost the impact of our analysis, we further extended our analysis to explore how different network features correlate with viewers engagement and appreciation of different episodes. This allowed us to derive an hierarchy of features that are associated with the audience response. All in all, our work show how network models may be able to capture social relations present in complex artificial worlds, thus providing a way to qualitatively model social interactions among fictional characters, hence allowing a minimal formal description of the unfolding of stories that can be instrumental in managing complex narratives.

[1]  Robert D. Kleinberg,et al.  Continuous-time model of structural balance , 2010, Proceedings of the National Academy of Sciences.

[2]  Hugues Bersini,et al.  Topology Analysis of Social Networks Extracted from Literature , 2015, PloS one.

[3]  L Albergante,et al.  Insights into Biological Complexity from Simple Foundations. , 2016, Advances in experimental medicine and biology.

[4]  U. Alon An introduction to systems biology : design principles of biological circuits , 2019 .

[5]  George R. R. Martin Song of ice and fire , 1996 .

[6]  S Redner,et al.  Dynamics of social balance on networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[8]  Frank Harary,et al.  A simple algorithm to detect balance in signed graphs , 1980, Math. Soc. Sci..

[9]  Sahin Albayrak,et al.  Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization , 2010, SDM.

[10]  F. Heider ATTITUDES AND COGNITIVE ORGANIZATION , 1977 .

[11]  M. Newman,et al.  Mixing Patterns and Community Structure in Networks , 2002, cond-mat/0210146.

[12]  Albert-László Barabási,et al.  Uncovering the role of elementary processes in network evolution , 2013, Scientific Reports.

[13]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .

[14]  Duncan J. Watts,et al.  The Structure and Dynamics of Networks: (Princeton Studies in Complexity) , 2006 .

[15]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[16]  T. Newman,et al.  Universal attenuators and their interactions with feedback loops in gene regulatory networks , 2016, bioRxiv.

[17]  F. Harary,et al.  STRUCTURAL BALANCE: A GENERALIZATION OF HEIDER'S THEORY1 , 1977 .

[18]  C. Altafini,et al.  Computing global structural balance in large-scale signed social networks , 2011, Proceedings of the National Academy of Sciences.

[19]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[20]  Igor M. Sokolov,et al.  Changing Correlations in Networks: Assortativity and Dissortativity , 2005 .

[21]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[22]  R. May Qualitative Stability in Model Ecosystems , 1973 .

[23]  Amir Bashan,et al.  Network physiology reveals relations between network topology and physiological function , 2012, Nature Communications.

[24]  Dirk Helbing,et al.  A network framework of cultural history , 2014, Science.

[25]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[26]  J Julian Blow,et al.  Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks , 2014, eLife.

[27]  Andrew Beveridge,et al.  Network of Thrones , 2016 .

[28]  P. L. Krapivsky,et al.  Social balance on networks : The dynamics of friendship and enmity , 2006 .