200 research outputs found
Discussion quality diffuses in the digital public square
Studies of online social influence have demonstrated that friends have
important effects on many types of behavior in a wide variety of settings.
However, we know much less about how influence works among relative strangers
in digital public squares, despite important conversations happening in such
spaces. We present the results of a study on large public Facebook pages where
we randomly used two different methods--most recent and social feedback--to
order comments on posts. We find that the social feedback condition results in
higher quality viewed comments and response comments. After measuring the
average quality of comments written by users before the study, we find that
social feedback has a positive effect on response quality for both low and high
quality commenters. We draw on a theoretical framework of social norms to
explain this empirical result. In order to examine the influence mechanism
further, we measure the similarity between comments viewed and written during
the study, finding that similarity increases for the highest quality
contributors under the social feedback condition. This suggests that, in
addition to norms, some individuals may respond with increased relevance to
high-quality comments.Comment: 10 pages, 6 figures, 2 table
Algorithmic statistics revisited
The mission of statistics is to provide adequate statistical hypotheses
(models) for observed data. But what is an "adequate" model? To answer this
question, one needs to use the notions of algorithmic information theory. It
turns out that for every data string one can naturally define
"stochasticity profile", a curve that represents a trade-off between complexity
of a model and its adequacy. This curve has four different equivalent
definitions in terms of (1)~randomness deficiency, (2)~minimal description
length, (3)~position in the lists of simple strings and (4)~Kolmogorov
complexity with decompression time bounded by busy beaver function. We present
a survey of the corresponding definitions and results relating them to each
other
Worldwide spreading of economic crisis
We model the spreading of a crisis by constructing a global economic network
and applying the Susceptible-Infected-Recovered (SIR) epidemic model with a
variable probability of infection. The probability of infection depends on the
strength of economic relations between the pair of countries, and the strength
of the target country. It is expected that a crisis which originates in a large
country, such as the USA, has the potential to spread globally, like the recent
crisis. Surprisingly we show that also countries with much lower GDP, such as
Belgium, are able to initiate a global crisis. Using the {\it k}-shell
decomposition method to quantify the spreading power (of a node), we obtain a
measure of ``centrality'' as a spreader of each country in the economic
network. We thus rank the different countries according to the shell they
belong to, and find the 12 most central countries. These countries are the most
likely to spread a crisis globally. Of these 12 only six are large economies,
while the other six are medium/small ones, a result that could not have been
otherwise anticipated. Furthermore, we use our model to predict the crisis
spreading potential of countries belonging to different shells according to the
crisis magnitude.Comment: 13 pages, 4 figures and Supplementary Materia
A complementary view on the growth of directory trees
Trees are a special sub-class of networks with unique properties, such as the
level distribution which has often been overlooked. We analyse a general tree
growth model proposed by Klemm {\em et. al.} (2005) to explain the growth of
user-generated directory structures in computers. The model has a single
parameter which interpolates between preferential attachment and random
growth. Our analysis results in three contributions: First, we propose a more
efficient estimation method for based on the degree distribution, which is
one specific representation of the model. Next, we introduce the concept of a
level distribution and analytically solve the model for this representation.
This allows for an alternative and independent measure of . We argue that,
to capture real growth processes, the estimations from the degree and the
level distributions should coincide. Thus, we finally apply both
representations to validate the model with synthetically generated tree
structures, as well as with collected data of user directories. In the case of
real directory structures, we show that measured from the level
distribution are incompatible with measured from the degree distribution.
In contrast to this, we find perfect agreement in the case of simulated data.
Thus, we conclude that the model is an incomplete description of the growth of
real directory structures as it fails to reproduce the level distribution. This
insight can be generalised to point out the importance of the level
distribution for modeling tree growth.Comment: 16 pages, 7 figure
Structure-guided design and optimization of small molecules targeting the protein-protein interaction between the von hippel-lindau (VHL) E3 ubiquitin ligase and the hypoxia inducible factor (HIF) alpha subunit with in vitro nanomolar affinities
E3 ubiquitin ligases are attractive targets in the ubiquitin-proteasome system, however, the development of small-molecule ligands has been rewarded with limited success. The von Hippel-Lindau protein (pVHL) is the substrate recognition subunit of the VHL E3 ligase that targets HIF-1α for degradation. We recently reported inhibitors of the pVHL:HIF-1α interaction, however they exhibited moderate potency. Herein, we report the design and optimization, guided by X-ray crystal structures, of a ligand series with nanomolar binding affinities
Googling the brain: discovering hierarchical and asymmetric network structures, with applications in neuroscience
Hierarchical organisation is a common feature of many directed networks arising in nature and technology. For example, a well-defined message-passing framework based on managerial status typically exists in a business organisation. However, in many real-world networks such patterns of hierarchy are unlikely to be quite so transparent. Due to the nature in which empirical data is collated the nodes will often be ordered so as to obscure any underlying structure. In addition, the possibility of even a small number of links violating any overall âchain of commandâ makes the determination of such structures extremely challenging. Here we address the issue of how to reorder a directed network in order to reveal this type of hierarchy. In doing so we also look at the task of quantifying the level of hierarchy, given a particular node ordering. We look at a variety of approaches. Using ideas from the graph Laplacian literature, we show that a relevant discrete optimization problem leads to a natural hierarchical node ranking. We also show that this ranking arises via a maximum likelihood problem associated with a new range-dependent hierarchical random graph model. This random graph insight allows us to compute a likelihood ratio that quantifies the overall tendency for a given network to be hierarchical. We also develop a generalization of this node ordering algorithm based on the combinatorics of directed walks. In passing, we note that Googleâs PageRank algorithm tackles a closely related problem, and may also be motivated from a combinatoric, walk-counting viewpoint. We illustrate the performance of the resulting algorithms on synthetic network data, and on a real-world network from neuroscience where results may be validated biologically
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
Effective-Range Expansion of the Neutron-Deuteron Scattering Studied by a Quark-Model Nonlocal Gaussian Potential
The S-wave effective range parameters of the neutron-deuteron (nd) scattering
are derived in the Faddeev formalism, using a nonlocal Gaussian potential based
on the quark-model baryon-baryon interaction fss2. The spin-doublet low-energy
eigenphase shift is sufficiently attractive to reproduce predictions by the
AV18 plus Urbana three-nucleon force, yielding the observed value of the
doublet scattering length and the correct differential cross sections below the
deuteron breakup threshold. This conclusion is consistent with the previous
result for the triton binding energy, which is nearly reproduced by fss2
without reinforcing it with the three-nucleon force.Comment: 21 pages, 6 figures and 6 tables, submitted to Prog. Theor. Phy
An Experimental Study of Cryptocurrency Market Dynamics
As cryptocurrencies gain popularity and credibility, marketplaces for
cryptocurrencies are growing in importance. Understanding the dynamics of these
markets can help to assess how viable the cryptocurrnency ecosystem is and how
design choices affect market behavior. One existential threat to
cryptocurrencies is dramatic fluctuations in traders' willingness to buy or
sell. Using a novel experimental methodology, we conducted an online experiment
to study how susceptible traders in these markets are to peer influence from
trading behavior. We created bots that executed over one hundred thousand
trades costing less than a penny each in 217 cryptocurrencies over the course
of six months. We find that individual "buy" actions led to short-term
increases in subsequent buy-side activity hundreds of times the size of our
interventions. From a design perspective, we note that the design choices of
the exchange we study may have promoted this and other peer influence effects,
which highlights the potential social and economic impact of HCI in the design
of digital institutions.Comment: CHI 201
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