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CNN Seminar - November

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  • UserSarah Teichman (MRC Laboratory of Molecular Biology) and Daniele Quercia (Horizon Researcher, Computer Labs)
  • ClockMonday 21 November 2011, 14:30-15:30
  • HouseKeynes Hall in Kings College.

If you have a question about this talk, please contact Petra Vertes.

Abstracts:

1) Network Analyses of Protein Structure, Evolution and Dynamics

Graph theory has made a huge impact on molecular biology in the past ten years as a paradigm for describing gene and protein interactions. In our group, we have used graph theoretical approaches to analyse the topology and evolution of genome-scale networks of both physical protein interactions [1] and transcriptional regulatory interactions [2]. This has shed light on the pervasive role of gene duplication in shaping both types of networks. We have also used networks to analyse dynamical processes in protein protein [3] and transcriptional regulatory interactions [4]. More recently, we have introduced the concept of networks to describe interactions between proteins within small complexes of known three-dimensional structure [5]. This has proved tremendously powerful in elucidating the principles of protein assembly and evolution.

[1] Pereira-Leal, J. & Teichmann, S.A. (2005) Novel specificities emerge by step-wise duplication of functional modules. Genome Res., 15, 552-559.

[2] Teichmann, S.A. & Babu, M.M. (2004) Gene Regulatory Network Growth by Duplication. Nature Genet., 36, 492-496.

[3] Levy, E.D., Boeri-Erba, E., Robinson, C.V. & Teichmann, S.A. (2008) Assembly reflects evolution of protein complexes. Nature, 453, 1262-5

[4] Luscombe, N.M., Babu, M.M., Yu, H., Snyder, M., Teichmann, S.A. & Gerstein, M. (2004) Genome-scale analysis of regulatory network dynamics. Nature, 431, 308-312.

[5] Levy, E.,D. Pereira-Leal, J., Chothia, C & Teichmann, S.A. (2006) 3DComplex: a structural classification of protein complexes. PLoS Comp Biol., 2, e155.

2) Personality and Language in Social Media

In Facebook, we studied the relationship between sociometric popularity (number of Facebook contacts) and personality traits [1]. We tested to which extent two prevalent viewpoints hold. That is, sociometrically popular Facebook users (those with many social contacts) are the ones whose personality traits either predict many offline (real world) friends or predict propensity to maintain superficial relationships. We found that the strongest predictor for number of friends in the real world (Extraversion) is also the strongest predictor for number of Facebook contacts. We then verified a widely held conjecture that has been put forward by literary intellectuals and scientists alike but has not been tested: people who have many social contacts on Facebook are the ones who are able to adapt themselves to new forms of communication, present themselves in likeable ways, and have propensity to maintain superficial relationships. We will see that there is no statistical evidence to support such a conjecture. In Twitter, instead, we tested whether users can be reduced to look-alike nodes (as most of the spreading models would assume) or, instead, whether they show individual differences that impact their popularity and influence. Again, one aspect that may differentiate users is their character and personality. For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism) [2]. Interestingly, we also find that popular users are ‘imaginative’ (high in Openness), while influentials tend to be ‘organised’ (high in Conscientiousness). We then show a way of accurately predicting a user’s personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user’s five personality traits with a root- mean-squared error below 0.88 on a [1,5] scale. Also, since it has been shown that personality is linked to the use of language (which is unobtrusively observable in tweets), we carry out a study of tweets and show that popular and influential users linguistically structure their tweets in specific ways [3]. This suggests that the popularity and influence of a Twitter account cannot be simply traced back to the graph properties of the network within which it is embedded, but also depends on the personality and emotions of the human being behind it. After introducing these studies, we will discuss theoretical implications of our findings as well as practical implications for viral marketing and social media security.

[1] The Personality of Popular Facebook Users. CSCW 2012 . http://dl.dropbox.com/u/6314563/papers/personality_fb.pdf

[2] Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. SocialCom 2011. http://www.cl.cam.ac.uk/dq209/publications/quercia11twitter.pdf

[3] In the Mood for Being Influential on Twitter. SocialCom 2011. http://www.cl.cam.ac.uk/dq209/publications/quercia11mood.pdf

This talk is part of the Cambridge Networks Network (CNN) series.

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