881 research outputs found
Reaction-Diffusion Processes on Interconnected Scale-Free Networks
We study the two particle annihilation reaction on
interconnected scale free networks, using different interconnecting strategies.
We explore how the mixing of particles and the process evolution are influenced
by the number of interconnecting links, by their functional properties, and by
the interconnectivity strategies in use. We show that the reaction rates on
this system are faster than what was observed in other topologies, due to the
better particle mixing which suppresses the segregation effect, inline with
previous studies performed on single scale free networks.Comment: 11 pages, 5 figure
The spatial component of R&D networks
We study the role of geography in R&D networks by means of a quantitative,
micro-geographic approach. Using a large database that covers international R&D
collaborations from 1984 to 2009, we localize each actor precisely in space
through its latitude and longitude. This allows us to analyze the R&D network
at all geographic scales simultaneously. Our empirical results show that
despite the high importance of the city level, transnational R&D collaborations
at large distances are much more frequent than expected from similar networks.
This provides evidence for the ambiguity of distance in economic cooperation
which is also suggested by the existing literature. In addition we test whether
the hypothesis of local buzz and global pipelines applies to the observed R&D
network by calculating well-defined metrics from network theory.Comment: Working paper, 22 pages, 7 figure
The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives
Counterparty risk denotes the risk that a party defaults in a bilateral
contract. This risk not only depends on the two parties involved, but also on
the risk from various other contracts each of these parties holds. In rather
informal markets, such as the OTC (over-the-counter) derivative market,
institutions only report their aggregated quarterly risk exposure, but no
details about their counterparties. Hence, little is known about the
diversification of counterparty risk. In this paper, we reconstruct the
weighted and time-dependent network of counterparty risk in the OTC derivatives
market of the United States between 1998 and 2012. To proxy unknown bilateral
exposures, we first study the co-occurrence patterns of institutions based on
their quarterly activity and ranking in the official report. The network
obtained this way is further analysed by a weighted k-core decomposition, to
reveal a core-periphery structure. This allows us to compare the activity-based
ranking with a topology-based ranking, to identify the most important
institutions and their mutual dependencies. We also analyse correlations in
these activities, to show strong similarities in the behavior of the core
institutions. Our analysis clearly demonstrates the clustering of counterparty
risk in a small set of about a dozen US banks. This not only increases the
default risk of the central institutions, but also the default risk of
peripheral institutions which have contracts with the central ones. Hence, all
institutions indirectly have to bear (part of) the counterparty risk of all
others, which needs to be better reflected in the price of OTC derivatives.Comment: 36 pages, 18 figures, 2 table
The network structure of city-firm relations
How are economic activities linked to geographic locations? To answer this
question, we use a data-driven approach that builds on the information about
location, ownership and economic activities of the world's 3,000 largest firms
and their almost one million subsidiaries. From this information we generate a
bipartite network of cities linked to economic activities. Analysing the
structure of this network, we find striking similarities with nested networks
observed in ecology, where links represent mutualistic interactions between
species. This motivates us to apply ecological indicators to identify the
unbalanced deployment of economic activities. Such deployment can lead to an
over-representation of specific economic sectors in a given city, and poses a
significant thread for the city's future especially in times when the
over-represented activities face economic uncertainties. If we compare our
analysis with external rankings about the quality of life in a city, we find
that the nested structure of the city-firm network also reflects such
information about the quality of life, which can usually be assessed only via
dedicated survey-based indicators.Comment: 12 pages, 4 figure
Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path structures and centralities
Recent research on temporal networks has highlighted the limitations of a
static network perspective for our understanding of complex systems with
dynamic topologies. In particular, recent works have shown that i) the specific
order in which links occur in real-world temporal networks affects causality
structures and thus the evolution of dynamical processes, and ii) higher-order
aggregate representations of temporal networks can be used to analytically
study the effect of these order correlations on dynamical processes. In this
article we analyze the effect of order correlations on path-based centrality
measures in real-world temporal networks. Analyzing temporal equivalents of
betweenness, closeness and reach centrality in six empirical temporal networks,
we first show that an analysis of the commonly used static, time-aggregated
representation can give misleading results about the actual importance of
nodes. We further study higher-order time-aggregated networks, a recently
proposed generalization of the commonly applied static, time-aggregated
representation of temporal networks. Here, we particularly define path-based
centrality measures based on second-order aggregate networks, empirically
validating that node centralities calculated in this way better capture the
true temporal centralities of nodes than node centralities calculated based on
the commonly used static (first-order) representation. Apart from providing a
simple and practical method for the approximation of path-based centralities in
temporal networks, our results highlight interesting perspectives for the use
of higher-order aggregate networks in the analysis of time-stamped network
data.Comment: 27 pages, 13 figures, 3 table
An ensemble perspective on multi-layer networks
We study properties of multi-layered, interconnected networks from an
ensemble perspective, i.e. we analyze ensembles of multi-layer networks that
share similar aggregate characteristics. Using a diffusive process that evolves
on a multi-layer network, we analyze how the speed of diffusion depends on the
aggregate characteristics of both intra- and inter-layer connectivity. Through
a block-matrix model representing the distinct layers, we construct transition
matrices of random walkers on multi-layer networks, and estimate expected
properties of multi-layer networks using a mean-field approach. In addition, we
quantify and explore conditions on the link topology that allow to estimate the
ensemble average by only considering aggregate statistics of the layers. Our
approach can be used when only partial information is available, like it is
usually the case for real-world multi-layer complex systems
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