31 research outputs found
The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles
We analyze the global structure of the world-wide air transportation network,
a critical infrastructure with an enormous impact on local, national, and
international economies. We find that the world-wide air transportation network
is a scale-free small-world network. In contrast to the prediction of
scale-free network models, however, we find that the most connected cities are
not necessarily the most central, resulting in anomalous values of the
centrality. We demonstrate that these anomalies arise because of the
multi-community structure of the network. We identify the communities in the
air transportation network and show that the community structure cannot be
explained solely based on geographical constraints, and that geo-political
considerations have to be taken into account. We identify each city's global
role based on its pattern of inter- and intra-community connections, which
enables us to obtain scale-specific representations of the network.Comment: Revised versio
Extracting the hierarchical organization of complex systems
Extracting understanding from the growing ``sea'' of biological and
socio-economic data is one of the most pressing scientific challenges facing
us. Here, we introduce and validate an unsupervised method that is able to
accurately extract the hierarchical organization of complex biological, social,
and technological networks. We define an ensemble of hierarchically nested
random graphs, which we use to validate the method. We then apply our method to
real-world networks, including the air-transportation network, an electronic
circuit, an email exchange network, and metabolic networks. We find that our
method enables us to obtain an accurate multi-scale descriptions of a complex
system.Comment: Figures in screen resolution. Version with full resolution figures
available at
http://amaral.chem-eng.northwestern.edu/Publications/Papers/sales-pardo-2007.pd
Modularity map of the network of human cell differentiation
Cell differentiation in multicellular organisms is a complex process whose
mechanism can be understood by a reductionist approach, in which the individual
processes that control the generation of different cell types are identified.
Alternatively, a large scale approach in search of different organizational
features of the growth stages promises to reveal its modular global structure
with the goal of discovering previously unknown relations between cell types.
Here we sort and analyze a large set of scattered data to construct the network
of human cell differentiation (NHCD) based on cell types (nodes) and
differentiation steps (links) from the fertilized egg to a crying baby. We
discover a dynamical law of critical branching, which reveals a fractal
regularity in the modular organization of the network, and allows us to observe
the network at different scales. The emerging picture clearly identifies
clusters of cell types following a hierarchical organization, ranging from
sub-modules to super-modules of specialized tissues and organs on varying
scales. This discovery will allow one to treat the development of a particular
cell function in the context of the complex network of human development as a
whole. Our results point to an integrated large-scale view of the network of
cell types systematically revealing ties between previously unrelated domains
in organ functions.Comment: 32 pages, 7 figure
Inheritance patterns in citation networks reveal scientific memes
Memes are the cultural equivalent of genes that spread across human culture
by means of imitation. What makes a meme and what distinguishes it from other
forms of information, however, is still poorly understood. Our analysis of
memes in the scientific literature reveals that they are governed by a
surprisingly simple relationship between frequency of occurrence and the degree
to which they propagate along the citation graph. We propose a simple
formalization of this pattern and we validate it with data from close to 50
million publication records from the Web of Science, PubMed Central, and the
American Physical Society. Evaluations relying on human annotators, citation
network randomizations, and comparisons with several alternative approaches
confirm that our formula is accurate and effective, without a dependence on
linguistic or ontological knowledge and without the application of arbitrary
thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical
Review
Indian generics producers, access to essential medicines and local production in Africa: an argument with reference to Tanzania
Much analysis of the supply chain for essential medicines to Africa assumes broad sustainability of low-cost generics supply from Indian manufacturers. We use Indian data and interviews to question this assumption. In a case study of Tanzania, we then argue for the necessity and feasibility of enhanced local production of essential medicines. We identify key industrial policy interventions, including industrial protection and active government purchasing; public goods including legislative and regulatory frameworks and training; and encouragement and facilitation of joint ventures. We show that a basis has been laid for these activities, and identify the urgency and difficulty of the policy challenge. There are lessons for the Tanzanian case from Indian industrial history, and policy space is provided by Tanzania's Least Developed Country status. Industrial and health policy can be further integrated to the benefit of Tanzania's citizens. The Tanzanian case has broader implications for African policymakers
A bio-inspired approach to attack graphs analysis
none5noComputer security has recently become more and more important as the world economy dependency from data has kept growing. The complexity of the systems that need to be kept secure calls for new models capable of abstracting the interdependencies among heterogeneous components that cooperate at providing the desired service. A promising approach is attack graph analysis, however the manual analysis of attack graphs is tedious and error prone. In this paper we propose to apply the metabolic network model to attack graphs analysis, using three interacting bio-inspired algorithms: topological analysis, flux balance analysis, and extreme pathway analysis. A developed framework for graph building and simulations as well as an introductory use case are also outlined.noneConti V.; Ruffo S.S.; Merlo A.; Migliardi M.; Vitabile S.Conti, V.; Ruffo, S. S.; Merlo, A.; Migliardi, M.; Vitabile, S
Social network models predict movement and connectivity in ecological landscapes
Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data