425 research outputs found
Flow graphs: interweaving dynamics and structure
The behavior of complex systems is determined not only by the topological
organization of their interconnections but also by the dynamical processes
taking place among their constituents. A faithful modeling of the dynamics is
essential because different dynamical processes may be affected very
differently by network topology. A full characterization of such systems thus
requires a formalization that encompasses both aspects simultaneously, rather
than relying only on the topological adjacency matrix. To achieve this, we
introduce the concept of flow graphs, namely weighted networks where dynamical
flows are embedded into the link weights. Flow graphs provide an integrated
representation of the structure and dynamics of the system, which can then be
analyzed with standard tools from network theory. Conversely, a structural
network feature of our choice can also be used as the basis for the
construction of a flow graph that will then encompass a dynamics biased by such
a feature. We illustrate the ideas by focusing on the mathematical properties
of generic linear processes on complex networks that can be represented as
biased random walks and also explore their dual consensus dynamics.Comment: 4 pages, 1 figur
Optimizing offshore wind export cable routing using GIS-based environmental heat maps
In the United States, there are plans to produce up to 30 GW of offshore wind power by the year 2030, resulting in numerous seabed lease areas which are currently going through the leasing or construction and operations phase. A key challenge associated with offshore wind is optimal routing and installation of the subsea power cables, which transmit power from the main offshore wind energy production area to a land-based station, where it connects to the electrical grid. By traversing a vast extent of the seafloor, the installation and operational phases of subsea power cables have the potential to result in a range of environmental impacts, which may negatively affect sensitive biological, physical, human and/or cultural resource receptors. Presented here is a case study from southeastern North Carolina to identify optimal seabed cable routes and coastal landfalls for a recently leased offshore wind farm by using a combination of publicly available data, coupled with standard environmental impact assessment methodologies and geographic information system (GIS)-based heat maps. The study identified a range of high-risk areas, in addition to a number of potential low-risk routes and landfall areas which minimize seabed user conflicts and impacts on environmentally sensitive locations. Although additional high-resolution and site-specific environmental, geological and biological surveys are required to develop a robust cable installation plan, the preliminary steps from this research optimize early-phase marine spatial planning for offshore wind projects and other similar subsea industries.</p
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
GEMSEC: Graph Embedding with Self Clustering
Modern graph embedding procedures can efficiently process graphs with
millions of nodes. In this paper, we propose GEMSEC -- a graph embedding
algorithm which learns a clustering of the nodes simultaneously with computing
their embedding. GEMSEC is a general extension of earlier work in the domain of
sequence-based graph embedding. GEMSEC places nodes in an abstract feature
space where the vertex features minimize the negative log-likelihood of
preserving sampled vertex neighborhoods, and it incorporates known social
network properties through a machine learning regularization. We present two
new social network datasets and show that by simultaneously considering the
embedding and clustering problems with respect to social properties, GEMSEC
extracts high-quality clusters competitive with or superior to other community
detection algorithms. In experiments, the method is found to be computationally
efficient and robust to the choice of hyperparameters
Universal Properties of Mythological Networks
As in statistical physics, the concept of universality plays an important,
albeit qualitative, role in the field of comparative mythology. Here we apply
statistical mechanical tools to analyse the networks underlying three iconic
mythological narratives with a view to identifying common and distinguishing
quantitative features. Of the three narratives, an Anglo-Saxon and a Greek text
are mostly believed by antiquarians to be partly historically based while the
third, an Irish epic, is often considered to be fictional. Here we show that
network analysis is able to discriminate real from imaginary social networks
and place mythological narratives on the spectrum between them. Moreover, the
perceived artificiality of the Irish narrative can be traced back to anomalous
features associated with six characters. Considering these as amalgams of
several entities or proxies, renders the plausibility of the Irish text
comparable to the others from a network-theoretic point of view.Comment: 6 pages, 3 figures, 2 tables. Updated to incorporate corrections from
EPL acceptance proces
Low linkage disequilibrium in wild Anopheles gambiae s.l. populations
<p>Abstract</p> <p>Background</p> <p>In the malaria vector <it>Anopheles gambiae</it>, understanding diversity in natural populations and genetic components of important phenotypes such as resistance to malaria infection is crucial for developing new malaria transmission blocking strategies. The design and interpretation of many studies here depends critically on Linkage disequilibrium (LD). For example in association studies, LD determines the density of Single Nucleotide Polymorphisms (SNPs) to be genotyped to represent the majority of the genomic information. Here, we aim to determine LD in wild <it>An. gambiae s.l</it>. populations in 4 genes potentially involved in mosquito immune responses against pathogens (<it>Gambicin</it>, <it>NOS</it>, <it>REL2 </it>and <it>FBN9</it>) using previously published and newly generated sequences.</p> <p>Results</p> <p>The level of LD between SNP pairs in cloned sequences of each gene was determined for 7 species (or incipient species) of the <it>An. gambiae </it>complex. In all tested genes and species, LD between SNPs was low: even at short distances (< 200 bp), most SNP pairs gave an r<sup>2 </sup>< 0.3. Mean r<sup>2 </sup>ranged from 0.073 to 0.766. In most genes and species LD decayed very rapidly with increasing inter-marker distance.</p> <p>Conclusions</p> <p>These results are of great interest for the development of large scale polymorphism studies, as LD generally falls below any useful limit. It indicates that very fine scale SNP detection will be required to give an overall view of genome-wide polymorphism. Perhaps a more feasible approach to genome wide association studies is to use targeted approaches using candidate gene selection to detect association to phenotypes of interest.</p
Bookselling online: an examination of consumer behaviour patterns.
Based upon empirical research, and using a range of methods, this paper examines the behaviour and experiences of consumers in online bookselling settings and offers comparison between online and offline (traditional) bookselling. The research finds that while the convenience of online bookshops is important, the key factors enticing consumers online are a combination of breadth of range, ease of access to obscure titles, as well as personalised recommendations and customer reviews. The research is of value to the book trade, highlighting consumer responses to widely adopted online marketing approaches. The research also contributes to scholarly knowledge in the fields of consumer behaviour, e-marketing and e-commerce in online bookselling, as well as providing findings which can be tested in other online settings, informing future theoretical research
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