19,050 research outputs found
Metrics for Graph Comparison: A Practitioner's Guide
Comparison of graph structure is a ubiquitous task in data analysis and
machine learning, with diverse applications in fields such as neuroscience,
cyber security, social network analysis, and bioinformatics, among others.
Discovery and comparison of structures such as modular communities, rich clubs,
hubs, and trees in data in these fields yields insight into the generative
mechanisms and functional properties of the graph.
Often, two graphs are compared via a pairwise distance measure, with a small
distance indicating structural similarity and vice versa. Common choices
include spectral distances (also known as distances) and distances
based on node affinities. However, there has of yet been no comparative study
of the efficacy of these distance measures in discerning between common graph
topologies and different structural scales.
In this work, we compare commonly used graph metrics and distance measures,
and demonstrate their ability to discern between common topological features
found in both random graph models and empirical datasets. We put forward a
multi-scale picture of graph structure, in which the effect of global and local
structure upon the distance measures is considered. We make recommendations on
the applicability of different distance measures to empirical graph data
problem based on this multi-scale view. Finally, we introduce the Python
library NetComp which implements the graph distances used in this work
Making GDPR Usable: A Model to Support Usability Evaluations of Privacy
We introduce a new model for evaluating privacy that builds on the criteria
proposed by the EuroPriSe certification scheme by adding usability criteria.
Our model is visually represented through a cube, called Usable Privacy Cube
(or UP Cube), where each of its three axes of variability captures,
respectively: rights of the data subjects, privacy principles, and usable
privacy criteria. We slightly reorganize the criteria of EuroPriSe to fit with
the UP Cube model, i.e., we show how EuroPriSe can be viewed as a combination
of only rights and principles, forming the two axes at the basis of our UP
Cube. In this way we also want to bring out two perspectives on privacy: that
of the data subjects and, respectively, that of the controllers/processors. We
define usable privacy criteria based on usability goals that we have extracted
from the whole text of the General Data Protection Regulation. The criteria are
designed to produce measurements of the level of usability with which the goals
are reached. Precisely, we measure effectiveness, efficiency, and satisfaction,
considering both the objective and the perceived usability outcomes, producing
measures of accuracy and completeness, of resource utilization (e.g., time,
effort, financial), and measures resulting from satisfaction scales. In the
long run, the UP Cube is meant to be the model behind a new certification
methodology capable of evaluating the usability of privacy, to the benefit of
common users. For industries, considering also the usability of privacy would
allow for greater business differentiation, beyond GDPR compliance.Comment: 41 pages, 2 figures, 1 table, and appendixe
Identification of Design Principles
This report identifies those design principles for a (possibly new) query and transformation
language for the Web supporting inference that are considered essential. Based upon these
design principles an initial strawman is selected. Scenarios for querying the Semantic Web
illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of
the query language to be designed and implemented by the REWERSE working group I4
Neural scaling laws for an uncertain world
Autonomous neural systems must efficiently process information in a wide
range of novel environments, which may have very different statistical
properties. We consider the problem of how to optimally distribute receptors
along a one-dimensional continuum consistent with the following design
principles. First, neural representations of the world should obey a neural
uncertainty principle---making as few assumptions as possible about the
statistical structure of the world. Second, neural representations should
convey, as much as possible, equivalent information about environments with
different statistics. The results of these arguments resemble the structure of
the visual system and provide a natural explanation of the behavioral
Weber-Fechner law, a foundational result in psychology. Because the derivation
is extremely general, this suggests that similar scaling relationships should
be observed not only in sensory continua, but also in neural representations of
``cognitive' one-dimensional quantities such as time or numerosity
Introduction
This book brings together for the first time the collected wisdom of international leaders in the theory and practice in the emerging field of cultural heritage crowdsourcing. It features eight accessible case studies of groundbreaking projects from leading cultural heritage and academic institutions, and four thought-‐provoking essays that reflect on the wider implications of this engagement for participants and on the institutions themselves
Projections of determinantal point processes
Let be a space filling-design of
points defined in . In computer experiments, an important property
seeked for is a nice coverage of . This property could
be desirable as well as for any projection of onto
for . Thus we expect that , which represents the design
with coordinates associated to any index set , remains
regular in where is the cardinality of . This paper
examines the conservation of nice coverage by projection using spatial point
processes, and more specifically using the class of determinantal point
processes. We provide necessary conditions on the kernel defining these
processes, ensuring that the projected point process is
repulsive, in the sense that its pair correlation function is uniformly bounded
by 1, for all . We present a few examples, compare
them using a new normalized version of Ripley's function. Finally, we
illustrate the interest of this research for Monte-Carlo integration
Analysis of a data matrix and a graph: Metagenomic data and the phylogenetic tree
In biological experiments researchers often have information in the form of a
graph that supplements observed numerical data. Incorporating the knowledge
contained in these graphs into an analysis of the numerical data is an
important and nontrivial task. We look at the example of metagenomic
data---data from a genomic survey of the abundance of different species of
bacteria in a sample. Here, the graph of interest is a phylogenetic tree
depicting the interspecies relationships among the bacteria species. We
illustrate that analysis of the data in a nonstandard inner-product space
effectively uses this additional graphical information and produces more
meaningful results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS402 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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