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I will be there for you: clique, character centrality, and community detection in Friends

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Abstract

Network science has proved useful in analyzing structure and dynamics of social networks in several areas. This paper aims at analyzing the relationships of characters in Friends, a famous sitcom. In particular, two important aspects are investigated. First, the structure of the communities (groups) is examined to shed light on how different methods for community detection perform. Second, besides investigating the static structure of the graphs as well as causality relationships, also temporal aspects were examined. After all, this show was aired for 10 years and thus plots, roles, and friendship patterns among the characters seem to have changed. Furthermore, this sitcom is frequently associated with distinguishing prior assumptions such as: all six characters are equally prominent; it has no dominant storyline; friendship as surrogate family. This paper uses tools from network theory to check whether these and other such assumptions can be quantified and proved correct, especially considering the temporal aspect, i.e., what happens in the sitcom along time. The main findings regarding the centrality and temporal aspects are: patterns in graphs representing different time slices of the show change; overall, degrees of the six friends are indeed nearly the same; however, contrarily to what is believed, in different situations, the magnitudes of degree centrality do change; betweenness centrality differs significantly for each character thus some characters are better connectors than others; there is a high difference regarding degrees of the six friends versus the rest of the characters, which points to a centralized network; there are strong indications that the six friends are part of a surrogate family. As for the presence of groups (communities) within the network, methods of different natures were investigated and compared (pairwise and also using various metrics, including plausibility). The multilevel method performs reasonably in general. Also, it stands out that those methods do not always agree, resulting in groups that are very different from method to method.

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Notes

  1. These data are available at https://github.com/anabazzan/friends.

  2. Available at http://igraph.org/python/.

  3. Henceforth, to follow the literature on networks, N is used to denote |V|.

  4. Interactions in scenes from episodes with flashbacks were considered part of these episodes; thus some interactions that happened in other seasons can be found in these graphs.

  5. These are: S1E18 (The One with All the Poker), S2E3 (The One Where Heckles Dies), S3E2 (The One Where No One’s Ready), S3E9 (The One with the Football), S3E16 (The One with the Morning After), S3E17 (The One Without the Ski Trip), S4E1 (The One with the Jellyfish), S4E12 (The One with the Embryos), S5E14 (The One Where Everybody Finds Out), S6E6 (The One on the Last Night), S6E9 (The One Where Ross Got High), S7E1 (The One with Monica’s Thunder), S7E8 (The One Where Chandler Doesn’t Like Dogs), S7E14 (The One Where They All Turn Thirty), S8E4 (The One with the Videotape), S8E9 (The One with the Rumor), S9E18 (The One with the Lottery), S10E4 (The One with the Cake), S10E10 (The One Where Chandler Gets Caught), S10E16 (The One with Rachel’s Going Away Party).

References

  • Ahn Y-Y, Han S, Kwak H, Moon S, Jeong H (2007) Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th international conference on world wide web, WWW ’07, New York. ACM, pp 835–844

  • Bazzan AL (2020) Similar yet different: the structure of social networks of characters in Seinfeld, Friends, How I met Your Mother, and The Big Bang Theory. RITA–Revista de Informática Teórica e Aplicada 27(2). https://seer.ufrgs.br/rita/article/view/RITA_VOL27_NR2_66

  • Beveridge A, Shan J (2016) Network of thrones. Math Horiz 23(4):18–22

    Article  MathSciNet  Google Scholar 

  • Chen R-G, Chen C-C, Chen C-M (2019) Unsupervised cluster analyses of character networks in fiction: community structure and centrality. Knowl Based Syst 163:800–810

    Article  Google Scholar 

  • Corten R (2012) Composition and structure of a large online social network in the Netherlands. PLoS One 7(4):e34760

    Article  Google Scholar 

  • Costa LF, Rodrigues FA, Travieso G, Boas PRV (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242

    Article  Google Scholar 

  • Csardi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695:1–9

    Google Scholar 

  • Danon L, Duch J, Diaz-Guilera A, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp

  • Edwards M, Mitchell L, Tuke J, Roughan M (2018) The one comparing narrative social network extraction techniques

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486

  • Heyd T (2010) How you guys doin’? Staged orality and emerging plural address in the television series Friends. Am Speech 85(1):33–66

    Article  Google Scholar 

  • Jasonov M (2017) Network science predicts who dies next in Game of Thrones. https://cns.ceu.edu/article/2017-07-08/network-science-predicts-who-dies-next-game-thrones. Accessed 9 Jan 2018

  • Jischa T (2004) They leave as they began: with a buzz. http://www.baltimoresun.com/bal-friends-buzz0502-story.html. Accessed 14 Dec 2017

  • Karinthy F (1929) Chain-links. Accessed 25 Aug 2018

  • Kauffmann M (2004) Friends. Final thoughts. https://youtu.be/L4HO_b7f7rU. Accessed 18 Dec 2017

  • Kolbert E (1994) The conception and delivery of a sitcom: everyone’s a critic. http://www.nytimes.com/1994/05/09/arts/the-conception-and-delivery-of-a-sitcom-everyone-s-a-critic.html?pagewanted=1. Accessed 16 Jan 2018

  • Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E 80(5):056117

    Article  Google Scholar 

  • Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110

    Article  Google Scholar 

  • Lauer M (2004) http://www.nbcnews.com/id/4899445/. Accessed 26 Dec 2017

  • Marshall LM (2007) “I’ll be there for you” if you are just like me: an analysis of hegemonic social structures in “friends”. Ph.D. thesis, Bowling Green State University

  • McCarroll C (2004) A ’family’ sitcom for gen X - ’Friends’ cast a new TV mold. https://www.csmonitor.com/2004/0506/p01s01-ussc.html. Accessed 14 Dec 2017

  • Melcher K (2017) The one about gender and sexuality: an examination of the television series friends. https://our.oakland.edu/handle/10323/4530. Accessed 3 Feb 2018

  • Milgram S (1967) The small world problem. Psychol Today 2

  • Nan C-J, Kim K-M, Zhang B-T (2015) Social network analysis of TV drama characters via deep concept hierarchies. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, ASONAM ’15, New York. ACM, pp 831–836

  • Prado SD, Dahmen SR, Bazzan AL, Carron PM, Kenna R (2016) Temporal analysis of literary texts. Adv Complex Syst 19(03):1–19

    Article  MathSciNet  Google Scholar 

  • Quaglio P (2009) Television dialogue: the Sitcom friends vs. natural conversation (Studies in Corpus Linguistics). John Benjamins Publishing Company

  • Seth Y (2017) Who was the lead character in Friends? The data science answer. https://yashuseth.blog/2017/12/29/data-analysis-lead-character-of-friends-data-science/. Accessed 30 Dec 2018

  • Sternbergh A (2016) Is ‘Friends’ still the most popular show on TV? http://www.vulture.com/2016/03/20-somethings-streaming-friends-c-v-r.html. Accessed 4 Feb 2018

  • Tan MSA, Ujum EA, Ratnavelu K (2014) A character network study of two Sci-Fi TV series. AIP Conf Proc 1588(1):246–251

    Article  Google Scholar 

  • Thompson K (2003) Storytelling in film and television. Harvard University Press, Cambridge

    Google Scholar 

  • Valente TW, Fujimoto K (2010) Bridging: locating critical connectors in a network. Soc Netw 32(3):212–220

    Article  Google Scholar 

  • Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Sci Rep 6(30750)

  • Yuta K, Ono N, Fujiwara Y (2007) A gap in the community-size distribution of a large-scale social networking site. arXiv e-prints

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Acknowledgements

Ana Bazzan is partially supported by The Brazilian Council of Research (CNPq), grant 307215/2017-2.

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  1. Instituto de Informática, UFRGS, Caixa Postal 15064, Porto Alegre, RS, 91.501-970, Brazil

    Ana L. C. Bazzan

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  1. Ana L. C. Bazzan
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Correspondence to Ana L. C. Bazzan.

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Communicated by Maria Aguieiras de Freitas.

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Bazzan, A.L.C. I will be there for you: clique, character centrality, and community detection in Friends. Comp. Appl. Math. 39, 192 (2020). https://doi.org/10.1007/s40314-020-01222-7

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