427 research outputs found
The Social Network of Contemporary Popular Musicians
In this paper we analyze two social network datasets of contemporary
musicians constructed from allmusic.com (AMG), a music and artists' information
database: one is the collaboration network in which two musicians are connected
if they have performed in or produced an album together, and the other is the
similarity network in which they are connected if they where musically similar
according to music experts. We find that, while both networks exhibit typical
features of social networks such as high transitivity, several key network
features, such as degree as well as betweenness distributions suggest
fundamental differences in music collaborations and music similarity networks
are created.Comment: 7 pages, 2 figure
Network Topology of an Experimental Futures Exchange
Many systems of different nature exhibit scale free behaviors. Economic
systems with power law distribution in the wealth is one of the examples. To
better understand the working behind the complexity, we undertook an empirical
study measuring the interactions between market participants. A Web server was
setup to administer the exchange of futures contracts whose liquidation prices
were coupled to event outcomes. After free registration, participants started
trading to compete for the money prizes upon maturity of the futures contracts
at the end of the experiment. The evolving `cash' flow network was
reconstructed from the transactions between players. We show that the network
topology is hierarchical, disassortative and scale-free with a power law
exponent of 1.02+-0.09 in the degree distribution. The small-world property
emerged early in the experiment while the number of participants was still
small. We also show power law distributions of the net incomes and
inter-transaction time intervals. Big winners and losers are associated with
high degree, high betweenness centrality, low clustering coefficient and low
degree-correlation. We identify communities in the network as groups of the
like-minded. The distribution of the community sizes is shown to be power-law
distributed with an exponent of 1.19+-0.16.Comment: 6 pages, 12 figure
Defining and identifying communities in networks
The investigation of community structures in networks is an important issue
in many domains and disciplines. This problem is relevant for social tasks
(objective analysis of relationships on the web), biological inquiries
(functional studies in metabolic, cellular or protein networks) or
technological problems (optimization of large infrastructures). Several types
of algorithm exist for revealing the community structure in networks, but a
general and quantitative definition of community is still lacking, leading to
an intrinsic difficulty in the interpretation of the results of the algorithms
without any additional non-topological information. In this paper we face this
problem by introducing two quantitative definitions of community and by showing
how they are implemented in practice in the existing algorithms. In this way
the algorithms for the identification of the community structure become fully
self-contained. Furthermore, we propose a new local algorithm to detect
communities which outperforms the existing algorithms with respect to the
computational cost, keeping the same level of reliability. The new algorithm is
tested on artificial and real-world graphs. In particular we show the
application of the new algorithm to a network of scientific collaborations,
which, for its size, can not be attacked with the usual methods. This new class
of local algorithms could open the way to applications to large-scale
technological and biological applications.Comment: Revtex, final form, 14 pages, 6 figure
Distance, dissimilarity index, and network community structure
We address the question of finding the community structure of a complex
network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept
of network random walking is introduced and a distance measure defined. Here we
calculate, based on this distance measure, the dissimilarity index between
nearest-neighboring vertices of a network and design an algorithm to partition
these vertices into communities that are hierarchically organized. Each
community is characterized by an upper and a lower dissimilarity threshold. The
algorithm is applied to several artificial and real-world networks, and
excellent results are obtained. In the case of artificially generated random
modular networks, this method outperforms the algorithm based on the concept of
edge betweenness centrality. For yeast's protein-protein interaction network,
we are able to identify many clusters that have well defined biological
functions.Comment: 10 pages, 7 figures, REVTeX4 forma
Parameter estimators of random intersection graphs with thinned communities
This paper studies a statistical network model generated by a large number of
randomly sized overlapping communities, where any pair of nodes sharing a
community is linked with probability via the community. In the special case
with the model reduces to a random intersection graph which is known to
generate high levels of transitivity also in the sparse context. The parameter
adds a degree of freedom and leads to a parsimonious and analytically
tractable network model with tunable density, transitivity, and degree
fluctuations. We prove that the parameters of this model can be consistently
estimated in the large and sparse limiting regime using moment estimators based
on partially observed densities of links, 2-stars, and triangles.Comment: 15 page
Effect of correlations on network controllability
A dynamical system is controllable if by imposing appropriate external
signals on a subset of its nodes, it can be driven from any initial state to
any desired state in finite time. Here we study the impact of various network
characteristics on the minimal number of driver nodes required to control a
network. We find that clustering and modularity have no discernible impact, but
the symmetries of the underlying matching problem can produce linear, quadratic
or no dependence on degree correlation coefficients, depending on the nature of
the underlying correlations. The results are supported by numerical simulations
and help narrow the observed gap between the predicted and the observed number
of driver nodes in real networks
Multiple dynamical time-scales in networks with hierarchically nested modular organization
Many natural and engineered complex networks have intricate mesoscopic
organization, e.g., the clustering of the constituent nodes into several
communities or modules. Often, such modularity is manifested at several
different hierarchical levels, where the clusters defined at one level appear
as elementary entities at the next higher level. Using a simple model of a
hierarchical modular network, we show that such a topological structure gives
rise to characteristic time-scale separation between dynamics occurring at
different levels of the hierarchy. This generalizes our earlier result for
simple modular networks, where fast intra-modular and slow inter-modular
processes were clearly distinguished. Investigating the process of
synchronization of oscillators in a hierarchical modular network, we show the
existence of as many distinct time-scales as there are hierarchical levels in
the system. This suggests a possible functional role of such mesoscopic
organization principle in natural systems, viz., in the dynamical separation of
events occurring at different spatial scales.Comment: 10 pages, 4 figure
Hierarchical characterization of complex networks
While the majority of approaches to the characterization of complex networks
has relied on measurements considering only the immediate neighborhood of each
network node, valuable information about the network topological properties can
be obtained by considering further neighborhoods. The current work discusses on
how the concepts of hierarchical node degree and hierarchical clustering
coefficient (introduced in cond-mat/0408076), complemented by new hierarchical
measurements, can be used in order to obtain a powerful set of topological
features of complex networks. The interpretation of such measurements is
discussed, including an analytical study of the hierarchical node degree for
random networks, and the potential of the suggested measurements for the
characterization of complex networks is illustrated with respect to simulations
of random, scale-free and regular network models as well as real data
(airports, proteins and word associations). The enhanced characterization of
the connectivity provided by the set of hierarchical measurements also allows
the use of agglomerative clustering methods in order to obtain taxonomies of
relationships between nodes in a network, a possibility which is also
illustrated in the current article.Comment: 19 pages, 23 figure
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