Modeling, Analysis, and Inference in Social Network Formation
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Date
2021
Publication Type
Doctoral Thesis
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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.
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published
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Editor
Contributors
Examiner : Dörfler, Florian
Examiner : Jackson, M.
Examiner : Stadtfeld, Christoph
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Publisher
ETH Zurich
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Subject
Social networks; Social network analysis (SNA); Mathematical modelling; Human Behavior
Organisational unit
09478 - Dörfler, Florian / Dörfler, Florian
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