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Hello!

I thought this might be of interest to you:

http://mboudour.github.io/2015/10/28/Shakespeare's-Macbeth-Network.html

Social networks commonly emerge out of a huge variety of data
(physical, social, biological, medical, geographical, economical,
political, psychological, cultural, artistic, technological, obtained
from computers, social media, communication etc. etc.). The analysis
and visualization of complex social networks aims at a better
understanding of the underlying phenomena and processes wherever they
come from. However, this is a very complicated task given that
nowadays there is an easy access to large or complex data sets
(usually referred to as "big data") for the analysis of which
traditional (small-scale) processing applications are rather
inadequate. Among other methodologies developed in order to confront
such a challenge, machine learning techniques have been proven very
efficient in facilitating the extraction of network structures from
big data sets originating from a diverse range of sources. In
particular, in the era of mass digitization of documents, books and
all sorts of means of human communication, the repository of
literature, literary texts, fiction, drama, theatrical plays,
cinematographic films etc. provide a great opportunity for the
exploration of any hidden network structure inside all these media.
Thus in this article, as a typical example, we are going to consider
William Shakespeare's Macbeth and show how elementary machine learning
techniques (implemented through the Python programming language) might
be used in order to detect actors, ties (here understood as
conversational relationships) and attributes in the networks
interwoven inside the plot of the play. Our goal is to visualize such
networks at different levels (acts and scenes), assess the importance
and centrality of the playing characters in them and examine how the
attributes of characters interfere and mix with the network positions
they hold and the network ties (relationships) they sustain (hence,
evaluate the degree of homophily or assortativity of the considered
networks).

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