108 research outputs found
Large-scale structure of a nation-wide production network
Production in an economy is a set of firms' activities as suppliers and
customers; a firm buys goods from other firms, puts value added and sells
products to others in a giant network of production. Empirical study is lacking
despite the fact that the structure of the production network is important to
understand and make models for many aspects of dynamics in economy. We study a
nation-wide production network comprising a million firms and millions of
supplier-customer links by using recent statistical methods developed in
physics. We show in the empirical analysis scale-free degree distribution,
disassortativity, correlation of degree to firm-size, and community structure
having sectoral and regional modules. Since suppliers usually provide credit to
their customers, who supply it to theirs in turn, each link is actually a
creditor-debtor relationship. We also study chains of failures or bankruptcies
that take place along those links in the network, and corresponding
avalanche-size distribution.Comment: 17 pages with 8 figures; revised section VI and references adde
Detecting modules in dense weighted networks with the Potts method
We address the problem of multiresolution module detection in dense weighted
networks, where the modular structure is encoded in the weights rather than
topology. We discuss a weighted version of the q-state Potts method, which was
originally introduced by Reichardt and Bornholdt. This weighted method can be
directly applied to dense networks. We discuss the dependence of the resolution
of the method on its tuning parameter and network properties, using sparse and
dense weighted networks with built-in modules as example cases. Finally, we
apply the method to data on stock price correlations, and show that the
resulting modules correspond well to known structural properties of this
correlation network.Comment: 14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor
changes. v3: 3 references added, minor change
A shadowing problem in the detection of overlapping communities: lifting the resolution limit through a cascading procedure
Community detection is the process of assigning nodes and links in
significant communities (e.g. clusters, function modules) and its development
has led to a better understanding of complex networks. When applied to sizable
networks, we argue that most detection algorithms correctly identify prominent
communities, but fail to do so across multiple scales. As a result, a
significant fraction of the network is left uncharted. We show that this
problem stems from larger or denser communities overshadowing smaller or
sparser ones, and that this effect accounts for most of the undetected
communities and unassigned links. We propose a generic cascading approach to
community detection that circumvents the problem. Using real and artificial
network datasets with three widely used community detection algorithms, we show
how a simple cascading procedure allows for the detection of the missing
communities. This work highlights a new detection limit of community structure,
and we hope that our approach can inspire better community detection
algorithms.Comment: 14 pages, 12 figures + supporting information (5 pages, 6 tables, 3
figures
The Naming Game in Social Networks: Community Formation and Consensus Engineering
We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat.
Mech.: Theory Exp. P06014] in empirical social networks. This stylized
agent-based model captures essential features of agreement dynamics in a
network of autonomous agents, corresponding to the development of shared
classification schemes in a network of artificial agents or opinion spreading
and social dynamics in social networks. Our study focuses on the impact that
communities in the underlying social graphs have on the outcome of the
agreement process. We find that networks with strong community structure hinder
the system from reaching global agreement; the evolution of the Naming Game in
these networks maintains clusters of coexisting opinions indefinitely. Further,
we investigate agent-based network strategies to facilitate convergence to
global consensus.Comment: The original publication is available at
http://www.springerlink.com/content/70370l311m1u0ng3
The role of endogenous and exogenous mechanisms in the formation of R&D networks
We develop an agent-based model of strategic link formation in Research and Development (R&D)networks. Empirical evidence has shown that the growth of these networks is driven by mechanisms whichare both endogenous to the system (that is, depending on existing alliances patterns) and exogenous (that is, driven by an exploratory search for newcomer firms). Extant research to date has not investigated both mechanisms simultaneously in a comparative manner. To overcome this limitation, we develop a general modeling framework to shed light on the relative importance of these two mechanisms. We test our model against a comprehensive dataset, listing cross-country and cross-sectoral R&D alliances from 1984 to 2009. Our results show that by fitting only three macroscopic properties of the network topology, this framework is able to reproduce a number of micro-level measures, including the distributions of degree, local clustering, path length and component size, and the emergence of network clusters. Furthermore, by estimating the link probabilities towards newcomers and established firms from the data, we find that endogenous mechanisms are predominant over the exogenous ones in the network formation, thus quantifying the importance of existing structures in selecting partner firms
Router-level community structure of the Internet Autonomous Systems
The Internet is composed of routing devices connected between them and
organized into independent administrative entities: the Autonomous Systems. The
existence of different types of Autonomous Systems (like large connectivity
providers, Internet Service Providers or universities) together with
geographical and economical constraints, turns the Internet into a complex
modular and hierarchical network. This organization is reflected in many
properties of the Internet topology, like its high degree of clustering and its
robustness.
In this work, we study the modular structure of the Internet router-level
graph in order to assess to what extent the Autonomous Systems satisfy some of
the known notions of community structure. We show that the modular structure of
the Internet is much richer than what can be captured by the current community
detection methods, which are severely affected by resolution limits and by the
heterogeneity of the Autonomous Systems. Here we overcome this issue by using a
multiresolution detection algorithm combined with a small sample of nodes. We
also discuss recent work on community structure in the light of our results
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work
Characterization of metabolic interrelationships and in silico phenotyping of lipoprotein particles using self-organizing maps
Peer reviewe
Emergence of Bursts and Communities in Evolving Weighted Networks
Understanding the patterns of human dynamics and social interaction, and the
way they lead to the formation of an organized and functional society are
important issues especially for techno-social development. Addressing these
issues of social networks has recently become possible through large scale data
analysis of e.g. mobile phone call records, which has revealed the existence of
modular or community structure with many links between nodes of the same
community and relatively few links between nodes of different communities. The
weights of links, e.g. the number of calls between two users, and the network
topology are found correlated such that intra-community links are stronger
compared to the weak inter-community links. This is known as Granovetter's "The
strength of weak ties" hypothesis. In addition to this inhomogeneous community
structure, the temporal patterns of human dynamics turn out to be inhomogeneous
or bursty, characterized by the heavy tailed distribution of inter-event time
between two consecutive events. In this paper, we study how the community
structure and the bursty dynamics emerge together in an evolving weighted
network model. The principal mechanisms behind these patterns are social
interaction by cyclic closure, i.e. links to friends of friends and the focal
closure, i.e. links to individuals sharing similar attributes or interests, and
human dynamics by task handling process. These three mechanisms have been
implemented as a network model with local attachment, global attachment, and
priority-based queuing processes. By comprehensive numerical simulations we
show that the interplay of these mechanisms leads to the emergence of heavy
tailed inter-event time distribution and the evolution of Granovetter-type
community structure. Moreover, the numerical results are found to be in
qualitative agreement with empirical results from mobile phone call dataset.Comment: 9 pages, 6 figure
Statistical HOmogeneous Cluster SpectroscopY (SHOCSY): an optimized statistical approach for clustering of ÂąH NMR spectral data to reduce interference and enhance robust biomarkers selection.
We propose a novel statistical approach to improve the reliability of (1)H NMR spectral analysis in complex metabolic studies. The Statistical HOmogeneous Cluster SpectroscopY (SHOCSY) algorithm aims to reduce the variation within biological classes by selecting subsets of homogeneous (1)H NMR spectra that contain specific spectroscopic metabolic signatures related to each biological class in a study. In SHOCSY, we used a clustering method to categorize the whole data set into a number of clusters of samples with each cluster showing a similar spectral feature and hence biochemical composition, and we then used an enrichment test to identify the associations between the clusters and the biological classes in the data set. We evaluated the performance of the SHOCSY algorithm using a simulated (1)H NMR data set to emulate renal tubule toxicity and further exemplified this method with a (1)H NMR spectroscopic study of hydrazine-induced liver toxicity study in rats. The SHOCSY algorithm improved the predictive ability of the orthogonal partial least-squares discriminatory analysis (OPLS-DA) model through the use of "truly" representative samples in each biological class (i.e., homogeneous subsets). This method ensures that the analyses are no longer confounded by idiosyncratic responders and thus improves the reliability of biomarker extraction. SHOCSY is a useful tool for removing irrelevant variation that interfere with the interpretation and predictive ability of models and has widespread applicability to other spectroscopic data, as well as other "omics" type of data
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