53,948 research outputs found
Faster Random Walks By Rewiring Online Social Networks On-The-Fly
Many online social networks feature restrictive web interfaces which only
allow the query of a user's local neighborhood through the interface. To enable
analytics over such an online social network through its restrictive web
interface, many recent efforts reuse the existing Markov Chain Monte Carlo
methods such as random walks to sample the social network and support analytics
based on the samples. The problem with such an approach, however, is the large
amount of queries often required (i.e., a long "mixing time") for a random walk
to reach a desired (stationary) sampling distribution.
In this paper, we consider a novel problem of enabling a faster random walk
over online social networks by "rewiring" the social network on-the-fly.
Specifically, we develop Modified TOpology (MTO)-Sampler which, by using only
information exposed by the restrictive web interface, constructs a "virtual"
overlay topology of the social network while performing a random walk, and
ensures that the random walk follows the modified overlay topology rather than
the original one. We show that MTO-Sampler not only provably enhances the
efficiency of sampling, but also achieves significant savings on query cost
over real-world online social networks such as Google Plus, Epinion etc.Comment: 15 pages, 14 figure, technical report for ICDE2013 paper. Appendix
has all the theorems' proofs; ICDE'201
The happiness paradox: your friends are happier than you
Most individuals in social networks experience a so-called Friendship
Paradox: they are less popular than their friends on average. This effect may
explain recent findings that widespread social network media use leads to
reduced happiness. However the relation between popularity and happiness is
poorly understood. A Friendship paradox does not necessarily imply a Happiness
paradox where most individuals are less happy than their friends. Here we
report the first direct observation of a significant Happiness Paradox in a
large-scale online social network of Twitter users. Our results reveal
that popular individuals are indeed happier and that a majority of individuals
experience a significant Happiness paradox. The magnitude of the latter effect
is shaped by complex interactions between individual popularity, happiness, and
the fact that users cluster assortatively by level of happiness. Our results
indicate that the topology of online social networks and the distribution of
happiness in some populations can cause widespread psycho-social effects that
affect the well-being of billions of individuals.Comment: 15 pages, 3 figures, 2 table
Modeling Evolutionary Dynamics of Lurking in Social Networks
Lurking is a complex user-behavioral phenomenon that occurs in all
large-scale online communities and social networks. It generally refers to the
behavior characterizing users that benefit from the information produced by
others in the community without actively contributing back to the production of
social content. The amount and evolution of lurkers may strongly affect an
online social environment, therefore understanding the lurking dynamics and
identifying strategies to curb this trend are relevant problems. In this
regard, we introduce the Lurker Game, i.e., a model for analyzing the
transitions from a lurking to a non-lurking (i.e., active) user role, and vice
versa, in terms of evolutionary game theory. We evaluate the proposed Lurker
Game by arranging agents on complex networks and analyzing the system
evolution, seeking relations between the network topology and the final
equilibrium of the game. Results suggest that the Lurker Game is suitable to
model the lurking dynamics, showing how the adoption of rewarding mechanisms
combined with the modeling of hypothetical heterogeneity of users' interests
may lead users in an online community towards a cooperative behavior.Comment: 13 pages, 5 figures. Accepted at CompleNet 201
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