14,645 research outputs found
Model of communities isolation at hierarchical modular networks
The model of community isolation was extended to the case when individuals
are randomly placed at nodes of hierarchical modular networks. It was shown
that the average number of blocked nodes (individuals) increases in time as a
power function, with the exponent depending on network parameters. The
distribution of time when the first isolated cluster appears is unimodal,
non-gaussian. The developed analytical approach is in a good agreement with the
simulation data
Comparison and validation of community structures in complex networks
The issue of partitioning a network into communities has attracted a great
deal of attention recently. Most authors seem to equate this issue with the one
of finding the maximum value of the modularity, as defined by Newman. Since the
problem formulated this way is NP-hard, most effort has gone into the
construction of search algorithms, and less to the question of other measures
of community structures, similarities between various partitionings and the
validation with respect to external information. Here we concentrate on a class
of computer generated networks and on three well-studied real networks which
constitute a bench-mark for network studies; the karate club, the US college
football teams and a gene network of yeast. We utilize some standard ways of
clustering data (originally not designed for finding community structures in
networks) and show that these classical methods sometimes outperform the newer
ones. We discuss various measures of the strength of the modular structure, and
show by examples features and drawbacks. Further, we compare different
partitions by applying some graph-theoretic concepts of distance, which
indicate that one of the quality measures of the degree of modularity
corresponds quite well with the distance from the true partition. Finally, we
introduce a way to validate the partitionings with respect to external data
when the nodes are classified but the network structure is unknown. This is
here possible since we know everything of the computer generated networks, as
well as the historical answer to how the karate club and the football teams are
partitioned in reality. The partitioning of the gene network is validated by
use of the Gene Ontology database, where we show that a community in general
corresponds to a biological process.Comment: To appear in Physica A; 25 page
Identifying communities by influence dynamics in social networks
Communities are not static; they evolve, split and merge, appear and
disappear, i.e. they are product of dynamical processes that govern the
evolution of the network. A good algorithm for community detection should not
only quantify the topology of the network, but incorporate the dynamical
processes that take place on the network. We present a novel algorithm for
community detection that combines network structure with processes that support
creation and/or evolution of communities. The algorithm does not embrace the
universal approach but instead tries to focus on social networks and model
dynamic social interactions that occur on those networks. It identifies
leaders, and communities that form around those leaders. It naturally supports
overlapping communities by associating each node with a membership vector that
describes node's involvement in each community. This way, in addition to
overlapping communities, we can identify nodes that are good followers to their
leader, and also nodes with no clear community involvement that serve as a
proxy between several communities and are equally as important. We run the
algorithm for several real social networks which we believe represent a good
fraction of the wide body of social networks and discuss the results including
other possible applications.Comment: 10 pages, 6 figure
Controlling nosocomial infection based on structure of hospital social networks
Nosocomial infection raises a serious public health problem, as implied by
the existence of pathogens characteristic to healthcare and hospital-mediated
outbreaks of influenza and SARS. We simulate stochastic SIR dynamics on social
networks, which are based on observations in a hospital in Tokyo, to explore
effective containment strategies against nosocomial infection. The observed
networks have hierarchical and modular structure. We show that healthcare
workers, particularly medical doctors, are main vectors of diseases on these
networks. Intervention methods that restrict interaction between medical
doctors and their visits to different wards shrink the final epidemic size more
than intervention methods that directly protect patients, such as isolating
patients in single rooms. By the same token, vaccinating doctors with priority
rather than patients or nurses is more effective. Finally, vaccinating
individuals with large betweenness centrality is superior to vaccinating ones
with large connectedness to others or randomly chosen individuals, as suggested
by previous model studies. [The abstract of the manuscript has more
information.]Comment: 12 figures, 2 table
Corporate payments networks and credit risk rating
Aggregate and systemic risk in complex systems are emergent phenomena
depending on two properties: the idiosyncratic risks of the elements and the
topology of the network of interactions among them. While a significant
attention has been given to aggregate risk assessment and risk propagation once
the above two properties are given, less is known about how the risk is
distributed in the network and its relations with the topology. We study this
problem by investigating a large proprietary dataset of payments among 2.4M
Italian firms, whose credit risk rating is known. We document significant
correlations between local topological properties of a node (firm) and its
risk. Moreover we show the existence of an homophily of risk, i.e. the tendency
of firms with similar risk profile to be statistically more connected among
themselves. This effect is observed when considering both pairs of firms and
communities or hierarchies identified in the network. We leverage this
knowledge to show the predictability of the missing rating of a firm using only
the network properties of the associated node
Detecting communities of triangles in complex networks using spectral optimization
The study of the sub-structure of complex networks is of major importance to
relate topology and functionality. Many efforts have been devoted to the
analysis of the modular structure of networks using the quality function known
as modularity. However, generally speaking, the relation between topological
modules and functional groups is still unknown, and depends on the semantic of
the links. Sometimes, we know in advance that many connections are transitive
and, as a consequence, triangles have a specific meaning. Here we propose the
study of the modular structure of networks considering triangles as the
building blocks of modules. The method generalizes the standard modularity and
uses spectral optimization to find its maximum. We compare the partitions
obtained with those resulting from the optimization of the standard modularity
in several real networks. The results show that the information reported by the
analysis of modules of triangles complements the information of the classical
modularity analysis.Comment: Computer Communications (in press
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