610,892 research outputs found
Characterizing interactions in online social networks during exceptional events
Nowadays, millions of people interact on a daily basis on online social media
like Facebook and Twitter, where they share and discuss information about a
wide variety of topics. In this paper, we focus on a specific online social
network, Twitter, and we analyze multiple datasets each one consisting of
individuals' online activity before, during and after an exceptional event in
terms of volume of the communications registered. We consider important events
that occurred in different arenas that range from policy to culture or science.
For each dataset, the users' online activities are modeled by a multilayer
network in which each layer conveys a different kind of interaction,
specifically: retweeting, mentioning and replying. This representation allows
us to unveil that these distinct types of interaction produce networks with
different statistical properties, in particular concerning the degree
distribution and the clustering structure. These results suggests that models
of online activity cannot discard the information carried by this multilayer
representation of the system, and should account for the different processes
generated by the different kinds of interactions. Secondly, our analysis
unveils the presence of statistical regularities among the different events,
suggesting that the non-trivial topological patterns that we observe may
represent universal features of the social dynamics on online social networks
during exceptional events
Signed Network Modeling Based on Structural Balance Theory
The modeling of networks, specifically generative models, have been shown to
provide a plethora of information about the underlying network structures, as
well as many other benefits behind their construction. Recently there has been
a considerable increase in interest for the better understanding and modeling
of networks, but the vast majority of this work has been for unsigned networks.
However, many networks can have positive and negative links(or signed
networks), especially in online social media, and they inherently have
properties not found in unsigned networks due to the added complexity.
Specifically, the positive to negative link ratio and the distribution of
signed triangles in the networks are properties that are unique to signed
networks and would need to be explicitly modeled. This is because their
underlying dynamics are not random, but controlled by social theories, such as
Structural Balance Theory, which loosely states that users in social networks
will prefer triadic relations that involve less tension. Therefore, we propose
a model based on Structural Balance Theory and the unsigned Transitive Chung-Lu
model for the modeling of signed networks. Our model introduces two parameters
that are able to help maintain the positive link ratio and proportion of
balanced triangles. Empirical experiments on three real-world signed networks
demonstrate the importance of designing models specific to signed networks
based on social theories to obtain better performance in maintaining signed
network properties while generating synthetic networks.Comment: CIKM 2018: https://dl.acm.org/citation.cfm?id=327174
Potential Networks, Contagious Communities, and Understanding Social Network Structure
In this paper we study how the network of agents adopting a particular
technology relates to the structure of the underlying network over which the
technology adoption spreads. We develop a model and show that the network of
agents adopting a particular technology may have characteristics that differ
significantly from the social network of agents over which the technology
spreads. For example, the network induced by a cascade may have a heavy-tailed
degree distribution even if the original network does not.
This provides evidence that online social networks created by technology
adoption over an underlying social network may look fundamentally different
from social networks and indicates that using data from many online social
networks may mislead us if we try to use it to directly infer the structure of
social networks. Our results provide an alternate explanation for certain
properties repeatedly observed in data sets, for example: heavy-tailed degree
distribution, network densification, shrinking diameter, and network community
profile. These properties could be caused by a sort of `sampling bias' rather
than by attributes of the underlying social structure. By generating networks
using cascades over traditional network models that do not themselves contain
these properties, we can nevertheless reliably produce networks that contain
all these properties.
An opportunity for interesting future research is developing new methods that
correctly infer underlying network structure from data about a network that is
generated via a cascade spread over the underlying network.Comment: To Appear in Proceedings of the 22nd International World Wide Web
Conference(WWW 2013
Storing data on RFID tags: A standards-based approach
Online social networks are gaining increasing economic importance in light of the rising number of
members. The numerous recent acquisitions priced at enormous amounts illustrate this development.
Therefore, the growing relevance of online social networks in science as well as in practise revealed
the need for adequate valuation models, which take into account the networks’ specific
characteristics. Thus, this article develops an economic model for valuation of online social networks.
The model allows the evaluation of whether the purchase prices on the market, which recently
amounted to millions, are justifiable. Finally, the practical application of the model is illustrated by an
example of the major European online social network XING.com
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