Modeling, Analysis, and Inference in Social Network Formation
dc.contributor.author
Pagan, Nicolo
dc.contributor.supervisor
Dörfler, Florian
dc.contributor.supervisor
Jackson, M.
dc.contributor.supervisor
Stadtfeld, Christoph
dc.date.accessioned
2021-08-19T05:42:24Z
dc.date.available
2021-08-18T12:42:12Z
dc.date.available
2021-08-19T05:42:24Z
dc.date.issued
2021
dc.identifier.uri
http://hdl.handle.net/20.500.11850/501329
dc.identifier.doi
10.3929/ethz-b-000501329
dc.description.abstract
Human beings are social creatures that are naturally looking for, and in fact need, social
interactions to maintain a healthy life and mindset. In the past couple of decades, the
advent of social media platforms has facilitated maintaining relationships with family
and friends even across long distances, but it has also affected people's behavior in their
social activity, e.g., allowing them to connect with others they do not even know in
real life. On the other hand, the growing amount of available data is giving a unique
opportunity for the analysis of the social network structures as well as of the underlying
human social behavior.
Social networks constitute highly complex systems because of their size, the inner
complexity of their components (human beings), and the feedback and cascade effects
due to the inter-dependency between the individual behavior and the network structure.
Furthermore, social networks are strongly coupled with other network systems, e.g., financial markets, technological systems, infrastructures. With the objective of advancing
our understanding of such a complex ecosystem, this thesis focuses on the modelling,
analysis, and inference of the social network formation process. At a high level, we
distinguish between online social networks, based on real-world friendships, and online
social networks, corresponding to our virtual identity on the social media platforms.
In the fi rst part of the thesis we analyze online social networks, whose topological
structure is fundamentally the result of the social behavior of the agents that locally
strive to optimize their position in the network. According to a number of socio-economic
theories grounded on extensive empirical research, improving one's own network position
can increase the individual's social capital in different forms, e.g., in terms of social
influence, brokerage opportunities, or social support. Hence, we consider a network
formation process in which actors' interest is defi ned as a parametric combination of different
socio-strategic incentives based on the network topology. We study this individual
networking behavior through the lens of game theory: following a utility maximization
principle, actors rationally choose their set of followees. While a more common objective
in strategic network formation games is to study the equilibria resulting from predefi ned
payoff functions, our goal is to rationalize the observed network structures in terms of the
unknown individual behavior of the actors. Our approach proves to be mathematically
tractable and statistically robust, and one of its main advantages is that it allows for
empirical validations on real-world historical data on well-studied online social networks,
as well as for comparison with well-known random networks models.
In the second part of the thesis, we turn our attention to today's most popular online
social networks. These platforms paved the way for the so-called Web 2.0, giving people
the opportunity to build a virtual user profi le and to share different forms of User-
Generated Content. Some of these online social networks, e.g., Facebook or LinkedIn,
require mutual consent for establishing a friendship, hence they mimic the characteristics
of our online social networks. Conversely, Twitter, YouTube, Instagram, TikTok, and
many other popular platforms are all directed networks, where social media users can
virtually follow real-life strangers. By means of the integrated search engines, they
can navigate others' user pro files, and their User-Generated Content, e.g., Tweets or
Instagram posts, which is typically catalogued by hashtags that facilitate its discovery
to the interested users. While the number of monthly active users in these platforms
has dramatically increased in the last decade, the scienti c literature on social network
formation models has not considered the User-Generated Content as main driving factor.
Our main contribution consists in formulating a network formation model based on the
attractiveness of the User-Generated Content. The individual linking decision is rooted
in a meritocratic principle that rewards those users that provide higher quality content.
The comparison between theoretical results and empirical data from Twitch, a popular
platform for online gamers, proves that our quality-based model captures a number of
realistic features of User-Generated Content-based online social networks.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
Social networks
en_US
dc.subject
Social network analysis (SNA)
en_US
dc.subject
Mathematical modelling
en_US
dc.subject
Human Behavior
en_US
dc.title
Modeling, Analysis, and Inference in Social Network Formation
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2021-08-19
ethz.size
186 p.
en_US
ethz.code.ddc
DDC - DDC::3 - Social sciences::300 - Social sciences
en_US
ethz.code.ddc
DDC - DDC::5 - Science::510 - Mathematics
en_US
ethz.identifier.diss
27552
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::09478 - Dörfler, Florian / Dörfler, Florian
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory::09478 - Dörfler, Florian / Dörfler, Florian
en_US
ethz.relation.cites
10.3929/ethz-b-000360647
ethz.relation.cites
10.3929/ethz-b-000360648
ethz.relation.cites
10.3929/ethz-b-000360651
ethz.relation.cites
10.3929/ethz-b-000383026
ethz.date.deposited
2021-08-18T12:42:18Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-08-19T05:42:33Z
ethz.rosetta.lastUpdated
2022-03-29T11:15:41Z
ethz.rosetta.versionExported
true
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Doctoral Thesis [29990]