96,016 research outputs found
Information filtering in complex weighted networks
Many systems in nature, society and technology can be described as networks,
where the vertices are the system's elements and edges between vertices
indicate the interactions between the corresponding elements. Edges may be
weighted if the interaction strength is measurable. However, the full network
information is often redundant because tools and techniques from network
analysis do not work or become very inefficient if the network is too dense and
some weights may just reflect measurement errors, and shall be discarded.
Moreover, since weight distributions in many complex weighted networks are
broad, most of the weight is concentrated among a small fraction of all edges.
It is then crucial to properly detect relevant edges. Simple thresholding would
leave only the largest weights, disrupting the multiscale structure of the
system, which is at the basis of the structure of complex networks, and ought
to be kept. In this paper we propose a weight filtering technique based on a
global null model (GloSS filter), keeping both the weight distribution and the
full topological structure of the network. The method correctly quantifies the
statistical significance of weights assigned independently to the edges from a
given distribution. Applications to real networks reveal that the GloSS filter
is indeed able to identify relevantconnections between vertices.Comment: 9 pages, 7 figures, 1 Table. The GloSS filter is implemented in a
freely downloadable software (http://filrad.homelinux.org/resources
Backbone of credit relationships in the Japanese credit market
We detect the backbone of the weighted bipartite network of the Japanese
credit market relationships. The backbone is detected by adapting a general
method used in the investigation of weighted networks. With this approach we
detect a backbone that is statistically validated against a null hypothesis of
uniform diversification of loans for banks and firms. Our investigation is done
year by year and it covers more than thirty years during the period from 1980
to 2011. We relate some of our findings with economic events that have
characterized the Japanese credit market during the last years. The study of
the time evolution of the backbone allows us to detect changes occurred in
network size, fraction of credit explained, and attributes characterizing the
banks and the firms present in the backbone.Comment: 14 pages, 8 figure
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Graph analysis of functional brain networks: practical issues in translational neuroscience
The brain can be regarded as a network: a connected system where nodes, or
units, represent different specialized regions and links, or connections,
represent communication pathways. From a functional perspective communication
is coded by temporal dependence between the activities of different brain
areas. In the last decade, the abstract representation of the brain as a graph
has allowed to visualize functional brain networks and describe their
non-trivial topological properties in a compact and objective way. Nowadays,
the use of graph analysis in translational neuroscience has become essential to
quantify brain dysfunctions in terms of aberrant reconfiguration of functional
brain networks. Despite its evident impact, graph analysis of functional brain
networks is not a simple toolbox that can be blindly applied to brain signals.
On the one hand, it requires a know-how of all the methodological steps of the
processing pipeline that manipulates the input brain signals and extract the
functional network properties. On the other hand, a knowledge of the neural
phenomenon under study is required to perform physiological-relevant analysis.
The aim of this review is to provide practical indications to make sense of
brain network analysis and contrast counterproductive attitudes
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally,
conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002
and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
- …