17 research outputs found
Benchmarking Measures of Network Influence
Identifying key agents for the transmission of diseases (ideas, technology,
etc.) across social networks has predominantly relied on measures of centrality
on a static base network or a temporally flattened graph of agent interactions.
Various measures have been proposed as the best trackers of influence, such as
degree centrality, betweenness, and -shell, depending on the structure of
the connectivity. We consider SIR and SIS propagation dynamics on a
temporally-extruded network of observed interactions and measure the
conditional marginal spread as the change in the magnitude of the infection
given the removal of each agent at each time: its temporal knockout (TKO)
score. We argue that the exhaustive approach of the TKO score makes it an
effective benchmark measure for evaluating the accuracy of other, often more
practical, measures of influence. We find that none of the common network
measures applied to the induced flat graphs are accurate predictors of network
propagation influence on the systems studied; however, temporal networks and
the TKO measure provide the requisite targets for the hunt for effective
predictive measures
Dynamical Properties of Interaction Data
Network dynamics are typically presented as a time series of network
properties captured at each period. The current approach examines the dynamical
properties of transmission via novel measures on an integrated, temporally
extended network representation of interaction data across time. Because it
encodes time and interactions as network connections, static network measures
can be applied to this "temporal web" to reveal features of the dynamics
themselves. Here we provide the technical details and apply it to agent-based
implementations of the well-known SEIR and SEIS epidemiological models.Comment: 29 pages, 15 figure
Detection and localization of change points in temporal networks with the aid of stochastic block models
A framework based on generalized hierarchical random graphs (GHRGs) for the
detection of change points in the structure of temporal networks has recently
been developed by Peel and Clauset [1]. We build on this methodology and extend
it to also include the versatile stochastic block models (SBMs) as a parametric
family for reconstructing the empirical networks. We use five different
techniques for change point detection on prototypical temporal networks,
including empirical and synthetic ones. We find that none of the considered
methods can consistently outperform the others when it comes to detecting and
locating the expected change points in empirical temporal networks. With
respect to the precision and the recall of the results of the change points, we
find that the method based on a degree-corrected SBM has better recall
properties than other dedicated methods, especially for sparse networks and
smaller sliding time window widths.Comment: This is an author-created, un-copyedited version of an article
accepted for publication/published in Journal of Statistical Mechanics:
Theory and Experiment. IOP Publishing Ltd is not responsible for any errors
or omissions in this version of the manuscript or any version derived from
it. The Version of Record is available online at
http://dx.doi.org/10.1088/1742-5468/2016/11/11330
Discrete hierarchy of sizes and performances in the exchange-traded fund universe
Using detailed statistical analyses of the size distribution of a universe of
equity exchange-traded funds (ETFs), we discover a discrete hierarchy of sizes,
which imprints a log-periodic structure on the probability distribution of ETF
sizes that dominates the details of the asymptotic tail. This allows us to
propose a classification of the studied universe of ETFs into seven size layers
approximately organized according to a multiplicative ratio of 3.5 in their
total market capitalization. Introducing a similarity metric generalising the
Herfindhal index, we find that the largest ETFs exhibit a significantly
stronger intra-layer and inter-layer similarity compared with the smaller ETFs.
Comparing the performance across the seven discerned ETF size layers, we find
an inverse size effect, namely large ETFs perform significantly better than the
small ones both in 2014 and 2015
Social Stability and Extended Social Balance - Quantifying the Role of Inactive Links in Social Networks
Structural balance in social network theory starts from signed networks with
active relationships (friendly or hostile) to establish a hierarchy between
four different types of triadic relationships. The lack of an active link also
provides information about the network. To exploit the information that remains
uncovered by structural balance, we introduce the inactive relationship that
accounts for both neutral and nonexistent ties between two agents. This
addition results in ten types of triads, with the advantage that the network
analysis can be done with complete networks. To each type of triadic
relationship, we assign an energy that is a measure for its average occupation
probability. Finite temperatures account for a persistent form of disorder in
the formation of the triadic relationships. We propose a Hamiltonian with three
interaction terms and a chemical potential (capturing the cost of edge
activation) as an underlying model for the triadic energy levels. Our model is
suitable for empirical analysis of political networks and allows to uncover
generative mechanisms. It is tested on an extended data set for the standings
between two classes of alliances in a massively multi-player on-line game
(MMOG) and on real-world data for the relationships between countries during
the Cold War era. We find emergent properties in the triadic relationships
between the nodes in a political network. For example, we observe a persistent
hierarchy between the ten triadic energy levels across time and networks. In
addition, the analysis reveals consistency in the extracted model parameters
and a universal data collapse of a derived combination of global properties of
the networks. We illustrate that the model has predictive power for the
transition probabilities between the different triadic states.Comment: 21 pages, 10 figure
Beyond the Power Law: Uncovering Stylized Facts in Interbank Networks
We use daily data on bilateral interbank exposures and monthly bank balance
sheets to study network characteristics of the Russian interbank market over
Aug 1998 - Oct 2004. Specifically, we examine the distributions of (un)directed
(un)weighted degree, nodal attributes (bank assets, capital and
capital-to-assets ratio) and edge weights (loan size and counterparty
exposure). We search for the theoretical distribution that fits the data best
and report the "best" fit parameters. We observe that all studied distributions
are heavy tailed. The fat tail typically contains 20% of the data and can be
mostly described well by a truncated power law. Also the power law, stretched
exponential and log-normal provide reasonably good fits to the tails of the
data. In most cases, however, separating the bulk and tail parts of the data is
hard, so we proceed to study the full range of the events. We find that the
stretched exponential and the log-normal distributions fit the full range of
the data best. These conclusions are robust to 1) whether we aggregate the data
over a week, month, quarter or year; 2) whether we look at the "growth" versus
"maturity" phases of interbank market development; and 3) with minor
exceptions, whether we look at the "normal" versus "crisis" operation periods.
In line with prior research, we find that the network topology changes greatly
as the interbank market moves from a "normal" to a "crisis" operation period.Comment: 17 pages, 9 figure
The CEP5 Peptide Promotes Abiotic Stress Tolerance, As Revealed by Quantitative Proteomics, and Attenuates the AUX/IAA Equilibrium in Arabidopsis.
Peptides derived from non-functional precursors play important roles in various developmental processes, but also in (a)biotic stress signaling. Our (phospho)proteome-wide analyses of C-TERMINALLY ENCODED PEPTIDE 5 (CEP5)-mediated changes revealed an impact on abiotic stress-related processes. Drought has a dramatic impact on plant growth, development and reproduction, and the plant hormone auxin plays a role in drought responses. Our genetic, physiological, biochemical, and pharmacological results demonstrated that CEP5-mediated signaling is relevant for osmotic and drought stress tolerance in Arabidopsis, and that CEP5 specifically counteracts auxin effects. Specifically, we found that CEP5 signaling stabilizes AUX/IAA transcriptional repressors, suggesting the existence of a novel peptide-dependent control mechanism that tunes auxin signaling. These observations align with the recently described role of AUX/IAAs in stress tolerance and provide a novel role for CEP5 in osmotic and drought stress tolerance
Historical trade integration : globalization and the distance puzzle in the long twentieth century
In times of ongoing globalization, the notion of geographic neutrality expects the impact of distance on trade to become ever more irrelevant. However, over the last three decades a wide range of studies has found an increase in the importance of distance during the second half of the twentieth century. This paper tries to reframe this discussion by characterizing the effect of distance over a broader historical point of view. To make maximal use of the available data, we use a state-space model to construct a bilateral index of historical trade integration. Our index doubles to quadruples yearly data availability before 1950, allowing us to expand the period of analysis to 1880-2011. This implies that the importance of distance as a determinant of the changing trade pattern can be analyzed for both globalization waves. In line with O’Rourke (2009) and Jacks, Meissner, and Novy (2011), we find that the first wave was marked by a strong, continuing decrease in the effect of distance. Initially, the second globalization wave started out similarly, but from the 1960s onwards the importance of distance starts increasing. Nevertheless this change is dwarfed by the strong decrease preceding it